# Review on Fault Diagnosis and Fault-Tolerant Control Scheme for Robotic
Manipulators: Recent Advances in AI, Machine Learning, and Digital Twin

Md Muzakkir Quamar and Ali Nasir, Senior Member IEEE Md Muzakkir Quamar is
with the Department of Controls and Instrumentation Engineering, King Fahd
University of Petroleum and Minerals, Dhahran, saudi Arabia e-mail:
g201806920<EMAIL_ADDRESS>Nasir is with the 1) Department of Controls and
Instrumentation Engineering and 2) Interdisciplinary Research Center for
Intelligent Manufacturing and Robotics, King Fahd University of Petroleum and
Minerals, Dhahran, saudi ArabiaReview Article

###### Abstract

This comprehensive review article delves into the intricate realm of fault-
tolerant control (FTC) schemes tailored for robotic manipulators. Our
exploration spans the historical evolution of FTC, tracing its development
over time, and meticulously examines the recent breakthroughs fueled by the
synergistic integration of cutting-edge technologies such as artificial
intelligence (AI), machine learning (ML), and digital twin technologies (DTT).
The article places a particular emphasis on the transformative influence these
contemporary trends exert on the landscape of robotic manipulator control and
fault tolerance.

By delving into the historical context, our aim is to provide a comprehensive
understanding of the evolution of FTC schemes. This journey encompasses the
transition from model-based and signal-based schemes to the role of sensors,
setting the stage for an exploration of the present-day paradigm shift enabled
by AI, ML, and DTT. The narrative unfolds as we dissect the intricate
interplay between these advanced technologies and their applications in
enhancing fault tolerance within the domain of robotic manipulators. Our
review critically evaluates the impact of these advancements, shedding light
on the novel methodologies, techniques, and applications that have emerged in
recent times.

The overarching goal of this article is to present a comprehensive perspective
on the current state of fault diagnosis and fault-tolerant control within the
context of robotic manipulators, positioning our exploration within the
broader framework of AI, ML, and DTT advancements. Through a meticulous
examination of both historical foundations and contemporary innovations, this
review significantly contributes to the existing body of knowledge, offering
valuable insights for researchers, practitioners, and enthusiasts navigating
the dynamic landscape of robotic manipulator control.

###### Index Terms:

Fault Tolerant Control, Fault Diagnosis, Fault Identification, Robotic
Manipulator, Artificial Intelligence, Machine learning, Digital Twin.

## Nomenclature

AEs

Autoencoders.

FTC

Fault tolerant control.

FD

Fault diagnosis.

FDI

Fault detection and identification.

AI

Artificial Intelligence.

DTT

Digital twin technologies.

DDT

Deep digital twin.

ML

Machine learning.

RL

Reinforcement learning.

DRL

Deep reinforcement learning.

FL

Fuzzy Logic.

TS

Takagi–Sugeno.

SVM

Support vector machine.

ANN

Artificial neural network.

RNN

Recurrent neural network.

CNN

Convolutional neural network.

DNN

Deep neural network.

RBF

Radial basis function.

MPC

Model predictive control.

FFT

Fast fourier transform.

DWT

Discrete wavelet transform

MLPN

Multi-layer perceptron network.

LMPC

Lyapunov-based model predictive controller.

AFT2BC

Adaptive fuzzy type-2 backstepping control.

SSA

Singular spectrum analysis.

HT

Hilbert Transform.

LMI

linear matrix inequality.

IoT

Internet of Things.

RBM

Restricted boltzmann machine.

DBM

Deep boltzmann machine.

##  I Introduction

In an era characterized by rapid industrialization and technological
advancement, robotic manipulators have emerged as indispensable tools in a
wide range of industries. These mechanical arms, resembling the dexterity and
precision of the human hand, have found applications in manufacturing,
healthcare, logistics, and even space exploration [1]. Their ability to
automate repetitive and intricate tasks has not only boosted productivity but
also reduced the risks associated with dangerous and complex operations [2].
However, the performance and reliability of these robotic systems are not
immune to unforeseen challenges and anomalies. This is where the concept of
Fault-Tolerant Control (FTC) comes into play.

The significance of robotic manipulators extends across diverse industries,
revolutionizing the way processes are carried out. In manufacturing, they
assemble intricate products with unparalleled precision and efficiency [3]. In
healthcare, they assist in minimally invasive surgeries, making procedures
safer and more effective [4]. In logistics, robotic arms are key players in
supply chain automation, handling tasks like sorting, packing, and even
delivery [5]. Additionally, robotic manipulators play a pivotal role in space
exploration, carrying out tasks that are too hazardous for human astronauts
[6]. However, as robotic manipulators become increasingly integral to these
industries, ensuring their reliability and safety has become paramount [7].
Even a minor fault or malfunction can lead to costly downtimes, product
defects, and, in some cases, pose safety risks to human operators. This is
where the importance of FTC becomes evident.

FTC is a field of study and practice dedicated to ensuring that robotic
manipulators can continue functioning in the presence of faults or anomalies.
These faults can be caused by a variety of factors, including hardware
failures, environmental changes, or even external interference. The primary
objective of FTC is to develop strategies that enable robotic manipulators to
maintain their operations or, at the very least, carry out graceful
degradation in the event of a fault [8]. This is achieved by employing various
control strategies and technologies to detect, isolate, and mitigate faults.

Fault diagnosis comprises three primary tasks: fault detection, fault
isolation, and fault identification [9]. The foundational step, fault
detection, involves determining whether a malfunction or fault exists in the
system and identifying when it occurred. Furthermore, fault isolation seeks to
pinpoint the specific location of the faulty component, while fault
identification focuses on discerning the type, shape, and size of the fault.
Accurately determining these factors is essential to enable the system to
promptly and effectively implement fault-tolerant responses, mitigating the
negative impacts caused by faulty components and ensuring normal system
operation. Further, appropriate fault-tolerant control is applied to recover
and reconfigure the system.

The integration of fault diagnosis and fault-tolerant control is depicted in
Figure 1. Essentially, real-time fault diagnosis plays a crucial role in
detecting system faults, specifying their locations, and assessing the
severity of malfunctions [10]. Utilizing this valuable information, the
supervision system can implement appropriate fault-tolerant measures, such as
compensating for faulty signals through adjustments to actuators/sensors,
fine-tuning or reconfiguring the controller, and even replacing faulty
components with redundant duplicates [11]. These actions are designed to
accommodate or eliminate the adverse effects resulting from faults, thereby
maintaining the operational integrity of the system.

Figure 1: Schematic block structure of FD and FTC.

As industries continue to rely on robotic manipulators for enhanced
productivity and safety, understanding and implementing FTC becomes
increasingly crucial. This article serves as a guide to both the historical
foundations and cutting-edge innovations in this field, shedding light on the
dynamic landscape of fault-tolerant control for robotic manipulators.

The main objective of this work is to provide a comprehensive overview of FTC
schemes for robotic manipulators. It delves into the historical development of
fault tolerance in control systems, discussing early concepts, model-based
approaches, and sensor-based strategies. Moreover, it explores recent
advancements that have been made possible by the integration of AI, ML, and
DTT. These innovations offer more sophisticated and adaptive solutions to
fault tolerance, ultimately enhancing the reliability and safety of robotic
manipulators.

###  I-A Related surveys

Over the past four decades, significant progress has been made in fault
diagnosis methods, fault-tolerant control techniques, and their applications
in various industries. In 2003, a comprehensive three-part survey on fault
diagnosis was published in [12, 13, 14]. A thorough overview of anomaly
detection research was presented in [15] with focus on identifying data
patterns that deviate from expected behavior with applications in cyber
security, military surveillance, and safety-critical system fault detection.
The work in [16, 17] offered comprehensive reviews of data-driven FD methods.
Additionally, in [18] a concise review focusing specifically on fault
detection in sensor networks is presented.

In 2015, a two-part survey on FD and FTC with detailed discussion over model-
based and signal-based approaches is done in [19], while FD with knowledge-
based and hybrid approaches is discused in [20]. A survey published in 2020,
discussed fault and failure causes in control systems, analyzing the latest
solutions for enhancing system resilience. It delves into recent advancements
in FDI techniques and active FTC designs. Additionally, a comprehensive
comparison of various FTC strategies is presented, highlighting their
advantages and disadvantages. A more recent discussion on FD and FTC
classification was published in 2021 [21].

A comparative study between active and passive FTC scheme is discussed in
detailed in [8]. A comprehensive review of FTCS is presented in [22], covering
system architecture, sensor, actuator fault types, and stability and
reliability analysis. A 2020 review on FTC for robots is discussed in detail
in [23]. Whereas a short review on FD for robots is presented in [24]. A
review on FTC for single-link flexible Manipulator System is presented in
[25]. Another review on fault detection and diagnosis of robotic systems was
published in 2018 in [26].

A 2020 review on FD methods for servo motors (demagnetization, short circuits,
rotor eccentricity) and drives (sensor faults, inverter faults) using signal
processing and AI is presented in [27]. Recent advancements are reviewed, and
future trends in joint servo system fault diagnosis are discussed. While a
more recent work in 2023 presented a review on AI-based fault diagnosis
techniques for various industrial machines [28]. It comprehensively analyzes
key publications from the past 20 years (2000-2020), encompassing diverse AI
techniques like ANN, DL, Fuzzy Logic (FL), and Support Vector Machines (SVMs).

The most recent review of FTC for Robotic Manipulators is presented in [29],
covering publications from 2003 to 2022. The review specifically focuses on
the utilization of AI techniques, including fuzzy logic, ANN, and sliding mode
control, in conjunction with other control methods to achieve robust and
resilient operation of robotic arms. A 2023 work with a comprehensive review
on the prognostics and health management of industrial robots using DL is
presented in [30]. This review delves into the key areas of fault detection,
diagnosis, isolation, and reconfiguration, all leveraging the power of ML, DL,
and RL techniques.

###  I-B Contributions of this Survey

My contribution to this review paper lies in addressing a crucial gap in the
field of FD and FTC for robotic manipulators. While extensive research exists
on both topics individually, a comprehensive review focusing specifically on
robotic manipulators and incorporating recent advancements in AI, ML, DL, RL,
DT does not exist in the literature as shown in Table I.

While a handful of studies have surveyed FD and FTC with a focus on AI and DL,
they remain fragmented. This review bridges this gap by providing an
integrated analysis of all these cutting-edge technologies, highlighting their
potential synergy and future applications for FD and FC in robotic
manipulators.

The main highlight of my work are as under:

  * •

Provides a comprehensive and unified resource: This review serves as a
valuable reference for researchers and engineers working on FD and FTC for
robotic manipulators.

  * •

Highlights the latest advancements: The review focuses on recent developments
in AI, ML, DL, RL, and DT, offering insights into their potential for
enhancing FD and FTC capabilities.

  * •

Synergy Analysis: Investigating the potential of synergistic combinations of
these technologies for enhanced performance and robust fault handling.

  * •

Promotes future research: By identifying promising research directions and
potential applications, the review encourages further exploration and
development in this rapidly evolving field.

TABLE I:  A comparision of the recent review on FD and FTC for robotic machines and manipulators using different techniques. Ref. |  AI |  ML |  DL |  RL |  DT |  Synergy
---|---|---|---|---|---|---
[29] |  ✓ |  ✓ |  - |  - |  - |  -
[31] |  - |  ✓ |  - |  - |  - |  -
[32] |  - |  - |  - |  ✓ |  - |  -
[30] |  - |  ✓ |  ✓ |  ✓ |  - |  -
[33] |  - |  - |  ✓ |  - |  ✓ |  -
This work |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓

###  I-C Organization

The structure of this article is organized to offer a detailed exploration of
FTC for robotic manipulators. We begin with a historical perspective, tracking
the evolution of fault tolerance strategies, and then delve into contemporary
developments. We examine the pivotal role of AI and ML in fault detection,
recovery, and human-in-the-loop control. We also explore the concept of DTT
and its applications in predictive maintenance and real-time simulation.

##  II Robotic Manipulator

Figure 2: Simplified block structure of a robotic system [30]

Robots have shed their initial image of clunky, industrial assistants,
evolving into highly dexterous, adaptable machines capable of tackling a vast
spectrum of tasks. From the cramped confines of automotive assembly lines to
the delicate intricacies of minimally invasive surgery, robots now navigate
environments both hazardous and intricate, exceeding the limitations of human
capabilities. This remarkable transformation owes much to the advancements in
mechanics, sensing, and control systems, granting robots not only dexterity
but also autonomy. As a result, their applications have expanded beyond the
factory floor, finding roles in fields as diverse as healthcare, underwater
exploration, and even household assistance.

Within the realm of industry, robots have become indispensable partners in
achieving high-precision tasks in manufacturing [34] and surgery [35]. These
complex machines, often sporting intricate robotic arms and a network of
sensors, are the workhorses of modern production lines and operating theatres
[36]. However, their intricate machinery, often featuring servo motors and
complex gear systems, leaves them susceptible to failure. Ensuring optimal
performance and minimizing downtime for these robotic workhorses requires
continuous monitoring and a deep understanding of their key components. Figure
2 provides a glimpse into its core: the power sources that fuel its actions,
the control computer that orchestrates its dance, and the mechanical framework
that serves as its muscular system. Within this framework lies a complex
ecosystem of pneumatic, hydraulic, and electrical actuators, sensors that
provide feedback, and an end-effector that serves as the robot’s hand,
interacting with the environment [37]. All these components work in unison,
guided by sophisticated software and safety functions, to translate the
digital commands of the control computer into precise movements [38].

However, the versatility of robots extends beyond mere movement. Programmable
robots tirelessly repeat pre-defined sequences of tasks [39], while computer-
programmable robots offer the additional flexibility of remote control and
complex operation [40]. Their structural configurations, as showcased in
Figure 3, can be tailored to specific needs, with simple non-servo robots
handling basic tasks and six-axis robots boasting impressive dexterity and
independent movement in each direction.

Despite these advancements, even the most sophisticated robots are not immune
to faults [41]. Timely detection of malfunctions is crucial, as they can not
only degrade performance and lead to failures but also accelerate wear and
tear on delicate machinery. While a faulty robot may still produce acceptable
results in the short term, its long-term efficiency and lifespan are
significantly compromised [2]. Therefore, understanding the types of faults
that can afflict different robot components and developing robust fault
detection and mitigation strategies are crucial for ensuring the smooth and
reliable operation of these tireless robotic workers.

Figure 3: A typical robotic manipulator with 6-dof [42]

##  III Historical Development of Fault-Tolerant Control

The fundamentals of fault tolerance in computer systems were outlined in [43],
which introduced the various error classifications and the use of redundancy
techniques to ensure reliable computer operation. Fault modeling and
prediction were also covered, as well as instances of fault-tolerant
computers. In the early 1990s, [44, 45], and [46] published the findings of
various fault-tolerant control system applications. A state-of-the-art
overview of FTC methods for control applications in the late 90’s was provided
in [47].

The historical development of FTC for robotic manipulators is a testament to
the relentless pursuit of improving reliability and safety in automated
systems [48, 49]. A substantial body of research was dedicated to the
advancement of FTC systems for application in manipulators tailored for space
exploration in [50, 51, 52, 53]. The foundational principles for crafting
fault-tolerant manipulators were elucidated in [54]. In this work,
sophisticated modeling tools and robust assessment techniques for robotic
systems were delineated. It comprehensively addressed design considerations
and conducted a meticulous reliability assessment. Drawing from the outcomes
of these analyses, a methodology for the design of fault-tolerant manipulators
was proposed. Particular emphasis was placed on formulating recommendations
for the development of a 10-degree-of-freedom robotic arm, suggesting the
utilization of two actuators at each joint.

Throughout the historical development of FTC, several influential researchers
and key developments have played pivotal roles in advancing the field. Notable
among these is the work of John J. Craig, [55, 56, 57] whose groundbreaking
research in robotics, including the development of the Denavit-Hartenberg
parameters [58, 59] and kinematic equations[60], laid the theoretical
foundation for control strategies in robotic manipulators. These developments
greatly influenced the field of FTC [61, 62], as understanding the robotic
system’s dynamics and kinematics is essential for fault detection and
mitigation.

Another significant milestone was the introduction of redundancy resolution
methods by researchers like M.Vidyasagar [63, 64, 65, 66]. Redundancy
resolution allows manipulators to continue functioning in the presence of
faults by reconfiguring the robot’s motions or altering the task space while
maintaining end-effector constraints [67, 68, 69, 70]. As the years
progressed, researchers also began incorporating sensor-based approaches into
FTC [19, 71]. By integrating advanced sensors, robotic systems could monitor
their state and environment in real-time, detect anomalies, and adapt control
strategies accordingly. This development further enhanced the fault-tolerance
capabilities of robotic manipulators.

In conclusion, FTC has grown over time by gradually moving from early ideas
that relied on hardware redundancy to more advanced methods that are based on
models and sensors [72, 21]. This evolution was driven by the collaborative
efforts of influential researchers who made significant contributions to the
field. These developments laid the foundation for contemporary advancements in
AI, ML, and DTT, which continue to shape the landscape of fault-tolerant
control for robotic manipulators. In the subsequent sections, we will briefly
discuss the emergence of model-based and sensor-based approaches.

###  III-A Model-Based Approaches to Fault-Tolerant Control and Diagnosis

The journey of FTC began with early concepts rooted in hardware redundancy and
basic fault detection mechanisms. In the nascent stages of robotics, engineers
sought to mitigate system failures by employing redundant components, also
known as hardware redundancy. These early robotics systems included spare
motors, sensors, or mechanical components that could be activated if a fault
was detected. While this approach provided some level of fault tolerance, it
had notable limitations [73, 2]. One of the key limitations was that it was
largely reactive.

Hardware redundancy allowed for system recovery after a fault was detected,
but it did not actively prevent or predict failures. Additionally, this method
added significant cost and complexity to the robotic system, as maintaining
and synchronizing redundant components proved to be a challenging endeavor
[74, 75, 76]. However, these early concepts laid the foundation for
understanding the importance of redundancy and fault detection in achieving
fault tolerance [77]. As the field of robotics and automation progressed,
researchers began to explore more sophisticated approaches [78, 79].

The evolution of FTC saw a significant shift towards model-based approaches
[19, 80, 81, 82, 83]. This paradigm relied on developing mathematical models
of the robotic system and its environment, allowing for a deeper understanding
of how the system behaves under various conditions. With these models, it
became possible to predict and anticipate faults and, subsequently, design
control strategies to address them proactively. Researchers in the field
started working on adaptive control and robust control methodologies, which
became cornerstones of model-based FTC.

Adaptive control aims to adjust control parameters in real time to adapt to
changes in the system due to faults or uncertainties. This dynamic approach
made it possible to maintain system performance in the face of unexpected
deviations [84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96]. On the other
hand, robust control strategies were designed to provide stability and
performance guarantees even in the presence of uncertainties or external
disturbances. Robust control methods are well-suited to applications where the
system’s operating conditions may change over time, which is often the case in
complex robotic manipulator environments [97, 98, 99, 100, 101, 102, 103, 104,
105, 106, 107].

Another application of model-based fault tolerance is fault isolation and
recovery. A model-based controller architecture for FTC is presented in [108].
The controller architecture is based on a general controller parameterization.
The FTC architecture consists of two main parts, a Fault Detection and
Isolation (FDI) part and a controller reconfiguration part. The theoretical
basis for the architecture is given followed by an investigation of the single
parts in the architecture. It is shown that the general controller
parameterization is central in connection with both fault diagnosis and
controller reconfiguration. Using controller parameterization creates a
structured way to switch between different controllers, especially when it
comes to the controller reconfiguration part. This also allows controller
switching using different sets of actuators and sensors.

Another model-based FTC is studied in [109] and a proactive fault-tolerant
Lyapunov-based model predictive controller (LMPC) that can effectively deal
with an incipient control actuator fault is proposed. This method for
proactive fault-tolerant control combines the unique stability and robustness
of LMPC with the ability to explicitly account for control actuator faults in
the formulation of the MPC. The theoretical results are applied to a chemical
process example, and different scenarios were simulated to demonstrate that
the proposed proactive fault-tolerant model predictive control method can
achieve practical stability and efficiently deal with a control actuator
fault.

The work in [110] introduced a strategy for detecting faults and a robust
control scheme for recovering from actuator faults in robot manipulators.
While in [111] a decentralized adaptive fuzzy control method based on the
impedance approach for coordinating multiple mobile manipulators using
Lyapunov synthesis is designed. In [112] a decentralized adaptive control
strategy for interconnected systems subject to bounded disturbances is
developed. A novel decentralized hybrid adaptive fuzzy control system was
devised for a class of large-scale uncertain nonlinear systems in [113].

In [114] presents the structure and measurement models for a redundant
parallel six-component force sensor. It examines its fault tolerance, outlines
calibration models for both fault-free and fault-tolerant scenarios, and
establishes an assembly strategy and testing platform for practical industrial
use. A collaborative robotic manipulator with motion constraints using real-
time finite-time fault-tolerant control based on recurrent neural networks
(RNN) is presented in [115].

Figure 4: Simplified architecture of Model based FTC [116]

A novel fault detection approach centered on isolating fault signatures
through filtered torque prediction error estimation is designed in [117]. In
[118], authors developed an adaptive fault tolerance control scheme for two
linked-wheeled mobile robots. Two fault-tolerant control strategies for robot
manipulators is designed in [119]. The first one relies on linear parameter-
varying systems, emphasizing post-fault stability when the robot halts after a
fault is detected, particularly in an underactuated configuration. The second
strategy is based on Markovian jump linear systems, ensuring stability even
when the robot is in motion upon fault detection and accommodating all
manipulator configurations within a unified model. Both strategies utilize
output-feedback H$\infty$ controllers.

Although model-based approaches offer a structured and systematic means to
detect, identify, and mitigate faults, they come with their own set of
challenges. The accuracy and scalability of explicit input-output models for
complex robotic systems, such as those found in modern manufacturing or
surgical applications, remain substantial challenges. Incorporating real-time
implementation while managing computational demands and adapting to diverse
operating conditions adds complexity to model-based fault tolerance.
Additionally, integrating model-based approaches with learning-based
techniques, ensuring optimal redundancy management, and handling unknown
faults are pivotal challenges that demand further exploration [120, 121, 122].

Looking towards the future, innovative approaches are needed to overcome these
challenges and propel model-based fault tolerance and diagnosis to new
heights. Advanced modeling techniques, such as the integration of physics-
based and data-driven models, can enhance the accuracy of fault tolerance
models. Future systems may benefit from the adoption of explainable AI
techniques, ensuring transparency and interpretability in diagnostic
decisions. Adaptive fault-tolerant control strategies that can dynamically
adjust to novel faults and evolving conditions represent a promising avenue
for research [123, 124, 125].

Furthermore, leveraging operational data for continuous model improvement,
exploring edge computing for efficient real-time implementation, and designing
human-centric fault-tolerant systems are critical future directions [126, 127,
128]. Autonomous fault recovery, where systems can automatically adapt to and
recover from faults without human intervention, presents an exciting frontier
for advancing model-based fault tolerance [129, 130]. Encouraging
interdisciplinary collaboration between experts in control theory, machine
learning, and domain-specific fields will be essential for developing holistic
solutions that address the multifaceted challenges inherent in model-based
fault tolerance and diagnosis for robotic manipulators.

###  III-B Signal-Based Approaches to Fault-Tolerant Control and Diagnosis

In the realm of fault diagnosis, signal-based methods have revolutionized
fault diagnosis, enabling real-time fault detection and monitoring in
intricate systems. Unlike conventional model-based approaches, which depend on
predefined input-output models, signal-based methods utilize measured signals
to pinpoint and diagnose faults in various processes. By analyzing deviations
from normal system behavior in these signals, signal-based fault tolerance and
diagnosis techniques extract features indicative of potential faults.
Diagnostic decisions are then made by analyzing these symptoms and drawing
upon prior knowledge of healthy system behaviors [131, 132].

A crucial aspect of signal-based fault diagnosis is the extraction of relevant
features from measured signals using various techniques such as time-domain
analysis, frequency-domain analysis, and time-frequency analysis [133]. Time-
domain features, such as mean and standard deviation, provide insights into
overall signal behavior. Frequency-domain analysis, employing methods like the
Fourier transform, identifies specific frequency components associated with
faults [134, 135]. Time-frequency analysis, achieved through wavelet
transforms, offers a detailed representation in both time and frequency
domains [136, 137]. For robotic manipulators, this involves scrutinizing
signals related to joint positions, velocities, torques, and sensor feedback.
Features extracted from these signals serve as indicators of potential faults,
offering insights into deviations from expected behavior [138, 24].

Symptom analysis systematically examines these extracted features to identify
fault patterns, relying on a profound understanding of relationships between
symptoms and fault presence [21]. In the case of robotic manipulators, a
thorough analysis of the extracted characteristics is necessary in order to
discern patterns that may serve as indicators of faults. This procedure
necessitates an in-depth understanding of the interconnections between certain
symptoms and the probable existence of anomalies [128]. Symptom analysis
serves as a fundamental component of efficient fault detection systems,
including various manifestations such as deviations in joint trajectories, an
unexpected increase in motor currents, or irregularities in end-effector
positions [139, 2].

In robotic manipulators, joint behaviors serve as vital indicators of system
health. Deviations from expected trajectories, whether due to wear and tear or
mechanical failures, are swiftly identified through the analysis of measured
signals. Signal-based fault diagnosis enables real-time monitoring of joint
positions and velocities. Following signal capture, various signal processing
techniques can be employed to extract sensitive features for fault diagnosis
[140, 141]. Industrial robots operate under diverse conditions, including
varying joint speeds, loading, and articulation. The inherent non-linear
dynamics of robot motion, characterized by acceleration, constant speed, and
deceleration, introduce challenges for conventional signal analysis methods
like fast fourier transform (FFT). Utilizing joint time-frequency techniques,
such as discrete wavelet transform (DWT) [142] or short-time Fourier transform
[143], becomes essential for accurate robot fault diagnosis, providing precise
information about energy distribution over frequency bands [144, 145]. These
techniques ensure the continued precision and accuracy of manipulator
movements, crucial for applications ranging from manufacturing to surgical
robotics.

Figure 5

Figure 5: Simplified architecture of Signal-based FTC [116]

Figure 5 ends

In [146], a fault detection and diagnosis study is conducted on a PUMA 560
industrial robot with six degrees of freedom and three links. The study
focuses on replacing one of the motors, specifically at joint two, with an
abnormal one. For the detection of vibration signals, three accelerometer
sensors capturing (X, Y, and Z) dimensions are employed in this joint. These
signals are then transmitted using a 16-bit NI data acquisition card to a PC
for processing in an environment that utilizes LabVIEW and Matlab. Signal
conditioning and noise removal are initially performed using the DWT.
Subsequently, the signals undergo further modification through the wavelet
transform to distinguish between high and low frequencies for multi-resolution
analysis.

In [147], an intelligent system is introduced for monitoring and diagnosing
gear faults in the PUMA 560 robot arm joint. The method employs the DWT in
conjunction with a multi-layer perceptron network (MLPN) for fault diagnosis
and classification. A significant neural network level ensures a 100

The study in [148] presents fault detection and diagnosis for two robot arms,
where faults at the joints induce oscillations at the arm’s tip. Acceleration
in multiple directions is analyzed to extract fault features, and simulations
for planar and space robots are demonstrated. Two signal processing techniques
for fault detection, namely DWT and the improved Slantlet transform is
utilised. Fault allocation and classification are performed using a multi-
layer perceptron artificial neural network. Results show that the Slantlet
transform with the multi-layer perceptron network outperforms DWT technique,
exhibiting superior performance, shorter processing time, and higher accuracy.

The work in [149], demonstrates the efficient use of encoder signals,
processed with Singular Spectrum Analysis (SSA) and Hilbert Transform (HT),
for observing the energy performance and health of industrial robot systems.
Extracting instantaneous amplitudes (IA) and instantaneous frequencies (IF)
using SSA and HT enhances the detection of weak oscillations and residuals,
crucial for health evaluation and fault detection in industrial robots. The
system’s efficiency is verified through numerical simulation and experimental
data, showcasing its potential as a promising tool for health monitoring in
industrial robots, surpassing traditional vibration-based schemes.

A Gaussian mixture model-based unsupervised fault detection framework for
efficient fault detection in industrial robots using current signals is
suggested in [150]. Initial signal preprocessing cleans raw current signals,
and motion-insensitive fault features reflecting industrial robot
deterioration are then extracted. These features are input into unsupervised
learning algorithms, demonstrating effective fault detection with high
accuracy, as validated by experimental data from industrial robot systems.

Predictive maintenance is a crucial aspect of Industry 4.0. A predictive
maintenance fault detection method is proposed in [151] using motor current
signal analysis for industrial robots. Various signal processing techniques
are utilized for the motor current generated by the Cartesian robots, which
reveals a promising fault index for detecting transmission system
functionality. Preliminary results show encouraging outcomes compared to
traditional spectral analysis. The real-time capabilities of signal-based
fault tolerance enable the implementation of predictive maintenance strategies
for robotic manipulators. By continuously monitoring signals related to joint
behaviors, torques, and sensor feedback, the diagnostic system can predict
potential failures before they escalate. This proactive approach minimizes
downtime, reduces maintenance costs, and enhances the overall lifespan of
robotic manipulators [152].

Signal-based fault tolerance is crucial for safe human-robot collaboration. By
monitoring force signals, robots can detect anomalies and take action to
protect humans. Challenges exist, including interpreting complex signals,
requiring diverse datasets, and integrating these methods into real-world
robots. Future research needs to focus on explainable AI, unsupervised
learning for fault detection, and novel signal processing methods.

##  IV Role of Sensors in Fault-Tolerant Control and Diagnosis

Modern equipment and machinery including robotic manipulators integrate an
array of sensors, including encoders, accelerometers, and force/torque
sensors. Model and signal-based fault tolerance excels in the realm of sensor
fusion, where data from multiple sensors is amalgamated for comprehensive
fault detection. Whether it’s detecting irregularities in joint movements or
identifying anomalies in end-effector forces, sensor fusion enhances the
diagnostic system’s ability to discern faults with precision. Sensor-based
fault tolerance and diagnosis have become increasingly pivotal in the field of
FTC [153, 154]. They provide real-time data that enables the detection and
diagnosis of faults, ultimately enhancing the overall fault tolerance of these
systems [155, 156, 157, 158]. In this section, we delve into the role of
sensors in FTC and their evolution, focusing on advancements in sensor
technology and how sensor data is integrated into control strategies to
improve fault tolerance.

Sensors serve as the sensory organs of robotic manipulators, providing
critical information about the manipulator’s state and its environment. These
sensors can range from simple encoders that measure joint angles to more
complex devices like accelerometers, force/torque sensors, and vision systems
[155]. The importance of sensors in FTC lies in their ability to detect
deviations from the expected behavior of the robotic system, which may result
from component failures, wear and tear, or external disturbances [159, 160].
When a fault occurs, sensors act as the system’s ”nervous system,” collecting
data on positions, velocities, forces, and other relevant parameters. This
data is then analyzed to identify anomalies or discrepancies between the
expected and actual states of the system.

The prevailing literature predominantly emphasizes actuator-based fault
diagnosis, yet it is imperative to underscore the importance of implementing
sensor-based fault detection and diagnosis. Sensor malfunctions can exert a
profound impact on manipulator control, as inaccuracies in the measured joint
angles or angular accelerations can potentially result in chaotic behavior or
damage to the robotic application [98]. Different control strategies are
applied to detect diagnose and reconfigure the sensor faults in a robotic
manipulator. Higher order sliding-mode observer is employed in [161] for the
purpose of diagnosing sensor faults in the manipulator. The work in [162, 163]
introduces novel fault detection methods that utilize sliding-mode observers
and fault tolerant control to identify faults in robot components. A fault-
tolerant control system employing a sliding mode controller and a linear
extended state observer for a 4-degree-of-freedom robotic manipulator is
developed in [164].

A fault-tolerant multi-sensor switching strategy for redundant sensor-equipped
robot manipulators is discussed in [154]. It employs a linear-parameter-
varying model to describe the robot’s nonlinear dynamics and employs a
zonotopic set-based robust fault detection (FD) mechanism to monitor the
system. The FD mechanism checks sensors for faults and activates a switching
controller to choose the best estimation from healthy sensors for control. If
a fault is detected, the strategy switches to healthy sensors for control.
Fault identification and isolation for robot manipulators with sensor
malfunction utilizing an adaptive observer approach is presented in [165].

Sensor fault detection and compensation for robotic manipulators with adaptive
observer and a terminal sliding mode control law based on a second-order
integral sliding surface is discussed in [166]. This method allows sensor
fault detection without the requirement of known bounds on fault value or its
derivative. It also enables rapid and fixed-time fault-tolerant control,
offering predefined performance via defined funnel bounds on tracking error.
Lyapunov stability analysis demonstrates the ultimate boundedness of
estimation errors with the proposed observer and the fixed-time stability of
the control system.

A fault diagnosis and fault tolerant control scheme is proposed in [153] for a
leader-follower multiple manipulator system with sensor faults under a network
communication topology. A learning observer is designed to estimate sensor
faults in follower manipulators, and a distributed multiple manipulator
synchronization controller is designed for each follower manipulator using
fault estimation and a radial basis function (RBF) neural network
approximator. The stability of the error system is analyzed using the Lyapunov
stability theorem. Observer-based fault-tolerant synchronized control of
multi-agent systems with faults is studied in [167, 168].

In [169] a novel adaptive descriptor observer and controller for FE and FTC of
discrete-time nonlinear stochastic systems with multiple time-varying state
delays and intermittent sensor and actuator faults is developed. The adaptive
descriptor observer estimates the error dynamics and performs n-step FE. The
active observer-based controller stabilizes the closed-loop system. A series
of delay-dependent sufficient conditions are derived using the delay-dependent
Lyapunov function and linear matrix inequalities. While in [170] a fuzzy
adaptive descriptor observer and controller is designed for FE and FTC of a
discrete time Takagi–Sugeno (T–S) fuzzy stochastic systems with multiple time-
varying state delays, intermittent sensor and actuator faults, nonlinear
dynamics, and exogenous disturbances.

A fault diagnosis strategy for industrial robotic manipulators based on
inverse dynamics-based feedback linearization is presented in [171]. The
strategy uses vision-based logic to detect sensor faults and a set of second-
order sliding mode unknown input observers to detect and isolate actuator
faults. The sliding-mode approach provides good stability, robustness, and
fault estimation accuracy. A vision sensor is used to distinguish between
sensor and actuator faults and to design a fault-tolerant control strategy in
the case of sensor faults. The strategy has been verified and validated
through simulation and experiments on an industrial robotic manipulator.

An integrated FD and FTC scheme for robotic manipulators with sensor faults is
proposed in [172]. The adaptive observer-based fault diagnosis scheme rapidly
and accurately estimates constant and time-varying faults, and uses linear
matrix inequality (LMI) method solves the design parameters. The fault-
tolerant controller combines proportional-derivative and sliding mode control
to track the desired trajectory using the back-stepping method.

Recent advances in sensor fault diagnosis in manipulators have enabled the
development of more reliable and robust control systems for a variety of
applications, such as manufacturing, robotic surgery, and space exploration. A
robust fault detection and isolation for sensors used in robot manipulators is
provided in [98]. A new hybrid robust FTC scheme is proposed in [173],
switching between a robust H$\infty$ controller and a non-singular terminal
sliding mode controller upon actuator fault detection. The system is first
linearized using feedback linearization. A fuzzy-based switching system is
used to switch controllers, and an adaptive joint unscented Kalman filter is
used for fault detection and diagnosis. The proposed method simultaneously
estimates system states and parameters. A Robust FTC for n-DOF serial
hydraulic manipulators under matched and mismatched uncertainties and sensor
faults is proposed in [174]. The system is modeled and a robust FE algorithm
based on extended state observer is proposed which achieves force and position
tracking stability.

###  IV-A Advancements in Sensor Technology

The utilization of sensor technology in recent times has been essential in
augmenting fault tolerance and fault detection capabilities across diverse
sectors. The integration of advanced sensors provides more accurate and real-
time data, enabling better monitoring and detection of anomalies in systems
[175]. A comprehensive review of the advancement of sensor technologies is
presented in [176]. These developments have significantly enhanced the ability
to detect and diagnose faults in robotic manipulators [177]. Here are several
ways in which advancements in sensor technology contribute to improved fault
tolerance and fault diagnosis:

  * •

Precision and Resolution: Modern sensors offer higher precision and
resolution, allowing for finer measurements. This level of accuracy is
critical for detecting subtle deviations in the robot’s behavior, which might
be indicative of early-stage faults [178].

  * •

Multi-Sensor Fusion: The integration of multiple sensors, known as multi-
sensor fusion, has become more prevalent. Combining data from different
sensors, such as encoders, force/torque sensors, and vision systems, provides
a more comprehensive view of the robot’s state and its interactions with the
environment [179].

  * •

Sensor Redundancy: Redundancy in sensor systems ensures that even if one
sensor fails, others can continue to provide data for fault detection and
control adaptation. This redundancy is particularly important for critical
applications where safety and reliability are paramount [23].

  * •

Smart and Adaptive Sensors: Smart sensors equipped with built-in processing
capabilities and adaptive functionalities can analyze data locally. This
reduces the need for continuous data transmission to a central processing
unit, minimizing communication delays and improving the efficiency of fault
diagnosis [180].

  * •

Integration with Internet of Things (IoT) Platforms: Sensors are integral to
IoT platforms, creating a network of interconnected devices. This
interconnectedness allows for centralized monitoring, data analysis, and
decision-making. IoT-based fault diagnosis systems leverage sensor data to
provide a holistic view of the entire system [181, 182].

###  IV-B Integration of Sensor Data into Fault-Tolerant Control

The data collected by sensors serves as the foundation for FD and FTC
strategies. The data set may consist of data collected from several sensors
integrated into the system as well as feedback data obtained from various
components of the system. These data set needs to be processed and analysed
for decision-making.

A comprehensive review on FDD strategies within a unified data-processing
framework, offering different data processing techniques on FDD in complex
systems is discussed [183]. A detailed review on Deep Transfer Learning Models
for Industrial Fault Diagnosis Using Vibration and Acoustic Sensors Data is
discussed in [184]. A review exploring Big Data Management Tools, Sensing and
Computing Technologies, using sensor data, Visual Perception and Environment
Mapping Algorithms in the Internet of Robotic Things is discussed in [185].

Relevant review [186, 187, 188] utilizing sensor data for fault detection and
diagnosis, suggest some key steps from data collection to fault diagnosis and
reconfiguration. The involved key steps are discussed below.

  * •

Data Collection: Sensors continuously gather data regarding the manipulator’s
position, orientation, joint velocities, joint torques, and any external
forces or torques acting on the system. The data is transmitted to the control
system in real time.

  * •

Data Analysis: The control system processes the sensor data to assess the
system’s state. This includes checking for inconsistencies or deviations that
may indicate a fault. Techniques like signal processing, data fusion, and
filtering are employed to enhance the quality of the data and extract relevant
information.

  * •

Fault Detection: Fault detection algorithms analyze the processed sensor data
to identify anomalies. These algorithms may use predefined thresholds or
machine learning techniques to detect deviations from the expected behavior.
For instance, a sudden change in joint torque values could be indicative of a
mechanical fault.

  * •

Diagnosis: Once a fault is detected, the control system’s diagnostic module
determines the nature and location of the fault. This step is crucial in
determining the appropriate response for fault tolerance.

  * •

Control Adaptation: With knowledge of the fault, the control system adjusts
its strategies to compensate for the fault’s effects. Depending on the fault’s
severity, these adaptations may range from simple adjustments to more complex
reconfiguration of control laws or trajectories.

  * •

Recovery and Graceful Degradation: In cases where full recovery is not
possible, the control system ensures graceful degradation, allowing the
robotic manipulator to continue its operation at a reduced level of
performance or functionality. This prevents a complete system shutdown and
minimizes disruptions.

Overall, sensor-based FTC leverages advancements in sensor technology to
detect and respond to faults in real time. By providing precise, real-time
information about the robot’s state, sensors enable control systems to enhance
fault tolerance and system reliability, making them a critical component of
modern robotic manipulators.

##  V Recent Advances in AI and Machine Learning

In recent years, the fields of AI and ML have made significant contributions
to the advancement of FTC, FD and FDI for complex systems and processes such
as manufacturing, chemical processing, and robotics. These technologies have
empowered robotic systems with the ability to detect and respond to faults in
a more intelligent and adaptive manner.

In this section, we will explore the applications of AI and ML in FTC,
focusing on the use of ML, in particular deep learning, for fault detection
and diagnosis, the role of reinforcement learning (RL) in fault recovery
strategies, and the integration of human-in-the-loop control systems with AI.

AI and ML technologies have transformed the landscape of FTC by providing
sophisticated tools for fault detection, diagnosis, and recovery. The key
applications of AI and ML in FTC include:

  * •

Anomaly Detection: AI algorithms, particularly those based on DL, have been
employed for anomaly detection. By training models on normal robotic behavior,
any deviation from the learned patterns can be flagged as an anomaly. This
allows for the early detection of faults and malfunctions.

  * •

Predictive Maintenance: ML models can predict when specific components of the
robotic system are likely to fail based on historical data and environmental
factors. This enables proactive maintenance to prevent faults and reduce
downtime.

  * •

Adaptive Control: AI and ML can adapt control strategies in real time based on
the observed behavior of the robotic system. This adaptability ensures that
the system continues to operate optimally in the presence of faults or
disturbances.

  * •

Fault Recovery: ML models, especially RL, enable the development of
intelligent fault recovery strategies. By learning optimal control policies,
robotic manipulators can autonomously recover from faults or deviations from
desired behavior.

###  V-A Artificial Intelligence for Fault-Tolerant Control

The field of robotics and AI, has the capacity to enhance and augment human
capabilities, leading to increased productivity. Furthermore, these
technologies are progressing beyond basic thinking and are now striving to
emulate human-like cognitive capacities [189]. AI approaches are extensively
advanced technologies that find utility in the domains of fault-tolerant
control and fault diagnostics for robotic systems. Fuzzy logic controllers
(FLC), Bayesian reasoning techniques, and artificial neural network (ANN)
models are among the AI technologies employed in fault-tolerant control and
problem diagnostics [190].

As early as 1987, the integration of AI in FTC emerged when Selkäinaho and
Halme proposed a real-time expert system for fault identification and
localization in [191]. In case of sensor failure, the system promptly
substituted the measurement with a predictive model output signal,
demonstrating success in a pilot test addressing physical flaws through AI.
The authors in [192] developed an AI model using adaptive fuzzy type-2
backstepping control (AFT2BC) method for robotic manipulators. AFT2BC
successfully controlled a PUMA560 robotic arm in a MATLAB simulation, even
when the robot’s kinematic configuration was changed to simulate axis failure
or load changes. The proposed control algorithm did not require prior
knowledge of the robot’s dynamic model, allowing it to operate effectively
under both model uncertainty and external disturbances.

The work in [193] proposed a FTC scheme using non-singular terminal synergetic
control and interval type-2 fuzzy satin bowerbird optimization (IT2FSBO).
Additionally, an adaptive augmented extended Kalman filter (A-AEKF) was
presented in [194] to detect, identify, and isolate actuator faults with noise
resilience.

In [195], authors introduced a neuro-fuzzy robot fault-detection algorithm
enabling control with an SRI [196] controller despite sensor or axis actuator
failures. The architecture utilized a multilayer perceptron trained with
backpropagation and a FLC block for detection and FTC. The ANN input layer
included 15 neurons, organized into 3 groups, assessing position, velocity,
and acceleration for each axis. Output neurons generated positions and
velocities for each axis, integrated with fuzzy logic block results. The
algorithm successfully identified damaged axes, tested on a simulation of the
ER5u robot in MATLAB. While in [196], the work proposed a FD system for a
5-DOF robotic arm using a neuro-fuzzy approach. The system comprises a MLP for
FD and a fuzzy logic rule base for fault type and location identification.
Simulation results demonstrate the effectiveness of the NF-based FD system.

The authors in [197] proposed a novel FD and FTC method for robotic
manipulators, combining an SVM-based neural adaptive high-order variable
structure observer (ANHWSO) with an AI modelled adaptive modern fuzzy
backstepping variable structure controller (AMFBVSC). The method demonstrated
improved fault identification performance compared to traditional approaches.
The work in [70] introduced a dual neural network approach for FD and FTC in
robotic manipulators, enabling control even when up to three axes fail
simultaneously. This was the first study to demonstrate FTC capability in case
multiple axes failure.

###  V-B Deep Learning for Fault Detection and Diagnosis

The remarkable performance of DL in diverse fields like biomedical research,
image recognition, natural language processing, and voice recognition has
captivated researchers worldwide in recent years [198, 199, 200, 201, 202].
Research in robotic fault detection has witnessed significant advancements
through DL techniques [203, 204, 205, 206]. DL algorithms boast immense
potential, capable of automatically extracting complex and intricate features
from raw data, eliminating the need for manual feature engineering [207, 208,
209]. This powerful ability to discover hierarchical features within vast and
multi-dimensional industrial data sets makes DL a highly promising tool for
predictive maintenance solutions [210, 211, 212, 213].

Traditional data-driven approaches heavily relied on manual feature
engineering, requiring signal processing expertise and significant human
intervention. These techniques struggled with large data volumes and lacked
real-time capabilities. DL models revolutionize this process by automatically
extracting and selecting relevant features directly from raw data, eliminating
the need for time-consuming and error-prone manual feature engineering. This
enables them to learn complex patterns and representations from the data
directly, leading to improved fault detection [214, 215, 216]. DL surpasses
traditional methods due to its ability to handle intricate patterns and non-
linear interactions in robotic systems, along with end-to-end learning,
facilitating the development of enhanced FDD. DL techniques incorporate
different DL architectures, including Restricted Boltzmann Machine (RBM) [30],
autoencoders (AEs) [217], convolutional neural networks (CNNs) [218, 219], and
recurrent neural networks (RNNs) [220]. These frameworks have been proven
highly effective for FDD in robotic manipulators.

RBM is a generative stochastic neural network framework. offer a powerful
unsupervised learning approach for fault detection. There aee two generative
deep neural network (DNN) models based on the RBM; namely, the deep belief
network (DBN), and deep Boltzmann machines (DBMs) [221]. RBMs excel at
uncovering hidden patterns in data, making them ideal for anomaly detection in
complex systems. Trained on normal behavior data, they identify deviations as
potential anomalies. This allows RBMs to act as pre-processors, extracting key
features from high-dimensional sensor data or generating compressed
representations. These features can then be fed into downstream models for
further analysis and decision-making [222, 223]. While not as widely used as
other DL frameworks in FTC/FDD, RBMs’ unique capabilities offer significant
potential for enhancing fault detection accuracy and reliability [224, 225].

AEs are powerful unsupervised learning tools that efficiently learn data
representations. They consist of two parts: an encoder that compresses the
input data and a decoder that reconstructs it [226, 227, 228]. Autoencoders
excel at compressing data while simultaneously capturing crucial information,
making them valuable for extracting meaningful features from sensor data in
FTC/FDD applications [229, 230, 231]. This feature extraction capability,
achieved through training on large datasets, allows the encoder to learn
representations that serve as valuable inputs for subsequent models like fault
classifiers or prognostics [232]. Additionally, AEs can reduce the
dimensionality of high-dimensional sensor data, simplifying processing and
storage while preserving crucial information. This compression into a lower-
dimensional latent space facilitates further analysis and model development
[233, 234].

CNN are primarily utilized for image-based fault detection, where visual data,
such as images captured by cameras, serves as the input [235, 236]. These
networks possess the remarkable ability to analyze images of robotic
manipulator components and identify visual anomalies, like cracks or
deformations, potentially signifying an underlying fault [237].

RNNs possess the remarkable ability to learn and exploit temporal dependencies
in time-series data generated by sensors, making them uniquely suited for
predicting and diagnosing faults [238]. By analyzing the evolving patterns in
sensor data, RNNs can effectively detect anomalies such as changes in joint
angles or torques, which might indicate mechanical faults [239]. These DL
models achieve unprecedented levels of accuracy and precision in fault
detection, surpassing traditional methods. Through extensive training on large
datasets, RNNs learn to recognize both common and rare fault patterns,
significantly enhancing the robustness of FTC systems. A comprehensive review
on RNN application for FTC/FD in mechanical system is discussed in [240].

FTC/FDD using DL has proved to be a fast-growing field, with a lot of studies
being conducted for robotic manipulators. Lee et al. [213] proposed an
adaptive fault detection approach for servo-motors, enabling robustness under
varying operating conditions. Adam et al. [241] developed a multi-fault
diagnosis approach utilizing a CNN algorithm, paving the way for the detection
of multiple concurrent faults. Rauf et al. [241] adopted a transfer learning-
based DL framework for efficient fault detection in industrial robots. Zhou et
al. [242] successfully applied a DL model for diagnosing faults in harmonic
reducers, demonstrating its effectiveness in identifying potential issues. Yin
et al. [243] designed a dual-driven transfer network specifically for fault
diagnosis in industrial robots, highlighting its potential for this
application. These advancements pave the way for more reliable and robust
robotic systems through efficient and accurate fault detection capabilities.

###  V-C Reinforcement Learning for Fault Recovery Strategies

Reinforcement learning (RL), a powerful class of ML algorithms, can
significantly enhance the accuracy of control parameter estimations and enable
the creation of adaptive controllers that learn and adapt online in real-time
[244, 245, 246]. RL, with its inherent intelligence and learning abilities,
interacts with its environment to discover the best strategy for maximizing
its value function. Popular learning algorithms include policy iteration,
value iteration, integral RL, and Q-learning [247, 248].

RL has emerged as a potent tool for developing intelligent fault recovery
strategies in robotic manipulators. In RL, an agent learns to interact with
its environment to maximize a reward signal. The agent takes actions, observes
the resulting state changes, and learns which actions lead to the best
outcomes over time. A RL-integrated active fault-tolerant control framework
for robotic manipulators with joint actuator faults is proposed in [249]. This
framework utilizes a neural network for fault diagnosis and RL for fault-
tolerant control. When an actuator fault is detected, the RL controller
generates compensation torques to maintain system safety and control
performance. This approach avoids relying on precise system models, making it
suitable for broader applications. The framework was evaluated on a 7-DOF
Panda manipulator in the MuJoCo simulator, demonstrating its effectiveness.

Zhu et.al in [250], propose a novel RL approach for fault-tolerant control of
flexible multijoint robots. The method utilizes a model-free adaptive
algorithm with parameter estimation and neural networks to achieve accurate
fault identification and compensation while simultaneously reducing
computational complexity. This is achieved through a specially designed
critic-actor mechanism with an event-triggered parameter selection strategy.
zhang et. al. in [251] presented a novel active FTC framework for robot
manipulators using learning algorithms. This approach, unlike conventional FTC
methods, leverages dynamic learning theory and radial basis function networks
to achieve accurate identification and learning of both uncertainties and
actuator faults.

The work in [252] proposes a FD and FTC scheme for redundant robot arms. It
combines deep RL (DRL) and sliding mode observers. The DRL detects and
isolates sensor faults, while the observers identify and compensate for
actuator faults. This approach improves the safety and reliability of robotic
systems. The work is simulated for a 7-DOF Franka Emika Panda robot arm using
PyBullet environment. Liu et. al. in [253] developed an adaptive FTC scheme
for MIMO nonlinear discrete-time systems utilizing RL. The proposed approach
significantly reduces learning parameters in the RL-based FTC policy via a
fast-updating law, enhancing efficiency and reducing computational burden. Its
effectiveness is demonstrated on a two-link planar robot manipulator,
showcasing the strength of this approach in achieving robust and efficient
FTC.

Based on literature [254, 255, 256] and capabilities of RL [257], it can be
said that in the context of FTC, RL can be applied as follows:

  * •

Learning Optimal Control Policies: An RL agent can learn optimal control
policies that help the robotic manipulator recover from specific types of
faults. By training in simulated environments, the agent can explore various
strategies for fault recovery and adapt its behavior in real time [258].

  * •

Adaptive Control: RL can be used for adaptive control, where the agent
continually adjusts control parameters to adapt to changing conditions and
faults. This dynamic control strategy allows the robot to maintain desired
performance even in the presence of uncertainties [259].

  * •

Incorporating Prior Knowledge: RL models can incorporate prior knowledge about
the robotic system’s dynamics and constraints, enhancing their efficiency in
fault recovery. For example, an RL agent can consider the robot’s kinematics
and dynamics to optimize control inputs. One notable advantage of RL is its
ability to handle complex, dynamic, and partially observable environments,
making it well-suited for real-world applications where faults may arise in
unpredictable ways. RL-based fault recovery strategies enable robotic
manipulators to adapt to changing conditions and recover gracefully from
faults, reducing the impact of failures on operations [32].

##  VI Digital Twin Technology in Fault-Tolerant Control

The confluence of DTT and FTC has revolutionized the reliability and
performance landscape within the smart manufacturing industry [260]. With
Industries 4.0 and 5.0 heavily reliant on robotic manipulators, this section
embarks on a thorough examination of DTT’s significance in the context of FTC,
both generally and specifically as applied to robotic systems. We will delve
into DTT’s pivotal role in predictive maintenance, preempting failures and
bolstering FTC strategies through real-time simulations [261].

DTT creates a virtual twin of a physical system replicating its structure,
behavior, and performance. Real-time data synchronizes the twins, enabling in-
depth monitoring, predictive analysis, and virtual testing of control
strategies [262, 263]. DTT bridges the physical-digital gap, providing a live
reflection of the robot’s state and performance. This real-time model empowers
FTC by predicting faults, enabling proactive interventions, and offering a
virtual sandbox for testing fault tolerance strategies. DTT’s high-fidelity
representation enhances system monitoring and performance analysis [264].

The work in [265] proposes a DT model to control a robotic arm’s positioning
error. The DT model, encompassing physical, data, model, functional, and
application layers, utilizes a low-cost attitude sensor for real-time
feedback. This allows the DT to detect and mitigate positioning errors by
adjusting motor angles. While in [266] a DT-enabled system for 3D positioning
and error compensation in robotic arms is designed. A virtual sensor, modeled
geometrically, exchanges information with the physical sensor, enabling real-
time pose comparison. Closed-loop alignment between physical data and virtual
outputs dynamically adjusts arm joints to minimize positioning errors. This
information mutuality drastically reduces angle calculation needs, leading to
an impressive 81.23% error reduction across diverse positions.

Predictive maintenance has emerged as a vital facet of fault tolerance,
allowing proactive actions to prevent failures in robotic manipulators.
Digital twins play a pivotal role in enabling predictive maintenance by
continuously monitoring the state of the physical system and assessing its
condition. A detailed review titled ”Deep digital twins for detection,
diagnostics and prognostics” is presented in [267]. This study centers on
developing a deep digital twin (DDT) using deep generative models, learning
the healthy data distribution directly from operational data at an asset’s
inception. Unlike traditional methods, DDT doesn’t depend on historical
failure data for estimating asset health, enabling predictive fault
maintenance.

Based on the literature [268, 269, 270], it can be said that the process of
leveraging DT for predictive maintenance encompasses the following key
elements:

  * •

Data Synchronization: Sensors placed on the physical robotic manipulator
collect data regarding its state, including joint angles, velocities,
temperatures, and other relevant parameters. This data is transmitted in real
time to the digital twin.

  * •

Analysis and Comparison: The digital twin processes and analyzes the received
data to create a dynamic representation of the physical system. Deviations
from the expected behavior are identified by comparing the real-time data with
the model’s predictions.

  * •

Early Fault Detection: By recognizing early signs of anomalies or performance
degradation, digital twins can flag potential issues that may lead to
failures. For instance, if a joint’s performance deteriorates beyond a certain
threshold, the digital twin can signal that maintenance is required.

  * •

Predictive Models: Machine learning algorithms can be integrated into the
digital twin to create predictive models. These models use historical data to
forecast when specific components of the robotic manipulator are likely to
fail or require maintenance.

  * •

Proactive Actions: Armed with predictive insights from the digital twin,
maintenance actions can be initiated in a timely and precise manner. This
preventive maintenance reduces the risk of unexpected failures and minimizes
downtime.

Predictive maintenance through digital twins offers a data-driven approach to
ensuring the reliability of robotic manipulators. It shifts the paradigm from
reactive maintenance, which addresses failures after they occur, to a
proactive strategy that identifies issues before they can disrupt operations.
Xu et. al. in [33] proposed a two-phase digital-twin-assisted fault diagnosis
method using deep transfer learning. A ML-based architecture for sensor FD and
FDI using DT is discussed in [271].

DT technology proves highly transformative in FTC by providing the ability to
establish a virtual, real-time simulation environment for robotic
manipulators. This virtual space replicates the physical system, enabling
risk-free testing, optimization, and the formulation of fault tolerance
strategies. The process of using DT for real-time simulation involves several
key components:

  * •

Dynamic Simulation: The DT uses real-time data from the physical robotic
manipulator to update its dynamic simulation. This simulation models the
manipulator’s kinematics, dynamics, and behavior, offering a real-time
representation of the system’s operation [272].

  * •

Control Algorithm Testing: Control strategies and fault tolerance algorithms
can be tested in the virtual environment using the digital twin. This testing
allows for the evaluation of various control parameters and strategies to
optimize fault tolerance [273].

  * •

Scenario Testing: DT facilitate the creation of diverse scenarios that mimic
real-world conditions and potential fault scenarios. These scenarios can be
used to assess how the robotic manipulator responds to various challenges and
disturbances [274].

  * •

Training and Optimization: ML techniques, including reinforcement learning,
can be employed to optimize control strategies and fault tolerance responses.
The DT provides a safe space for training and testing these algorithms [275].

  * •

Real-Time Response Analysis: During real-time simulation, the DT continuously
analyzes the robotic manipulator’s response to various inputs and
disturbances. This analysis aids in refining control strategies and enhancing
fault tolerance [276].

Digital twins provide a risk-free and efficient method for real-time
simulation to refine fault tolerance strategies. This approach enables
comprehensive testing and optimization in a controlled virtual environment,
minimizing the need for costly physical experiments and reducing risks
associated with real-world faults. They facilitate predictive maintenance,
preventing failures proactively, and serve as a versatile platform for testing
fault tolerance strategies. Integrating digital twins into robotic manipulator
systems ensures enhanced fault tolerance, reduced downtime, and improved
overall performance, establishing them as vital components in modern robotic
manipulator control and maintenance.

##  VII Integration of AI, ML, and Digital Twin

The seamless integration of AI, ML, and DTT in FD/FTC for robotic manipulators
represents a convergence that revolutionizes how these systems operate,
anticipate, and adapt to faults. This section explores the synergies between
these three technologies in FTC, presents case studies showcasing their
successful integration, and discusses the benefits and challenges associated
with this holistic approach.

Figure 6: A robotic arm framework integrating RL and DT [277]

###  VII-A Synergies Between AI, ML, and DTT in Fault-Tolerant Control

The integration of AI, ML, and DTT creates a powerful synergy that enhances
the fault tolerance of robotic manipulators.

  * •

AI-Powered Digital Twins: AI-enhanced digital twins have the ability to adapt,
learn, and predict. These digital replicas of the physical system can
incorporate AI algorithms to monitor and analyze real-time data from the
robot, identifying anomalies, predicting faults, and simulating fault
scenarios [278, 264]. For example, a digital twin equipped with AI can analyze
historical data and predict when a robot’s joint might experience excessive
wear, prompting preventive maintenance.

  * •

Machine Learning for Anomaly Detection: ML algorithms, integrated into the
digital twin, can learn patterns of normal system behavior. When deviations
from these patterns occur, the digital twin can flag anomalies. These
anomalies might signal incipient faults, prompting early intervention [279].

  * •

Real-Time Control Optimization: AI and ML can optimize control strategies in
real time based on data from the digital twin and physical robot [280]. This
adaptability enables the robot to continue performing optimally in the
presence of faults, such as motor failures or environmental disturbances.

  * •

Fault Recovery Strategies: RL algorithms can be employed within the digital
twin to train and optimize fault recovery strategies [281]. This learning
process is carried out in a simulated environment, where the digital twin
emulates the real robot’s behavior. As a result, the robot can autonomously
recover from various faults more effectively.

###  VII-B Benefits and Challenges Associated with This Integrated Approach

While the integration of AI, ML, and DTT in FTC offers numerous benefits, it
also presents challenges:

Benefits:

  * •

Enhanced Fault Tolerance: Integration enables better fault detection,
prediction, and recovery. This results in higher system reliability and
uptime.

  * •

Proactive Maintenance: Predictive maintenance through the analysis of
historical data and real-time monitoring reduces the risk of unexpected
failures, leading to cost savings and operational efficiency.

  * •

Optimized Control Strategies: Real-time optimization of control strategies,
driven by AI and ML, ensures optimal performance, even in the presence of
faults or disturbances.

  * •

Autonomous Recovery: RL-powered fault recovery allows robotic manipulators to
autonomously respond to faults, minimizing the need for human intervention.

  * •

Simulation for Testing: The digital twin provides a safe environment for
testing fault tolerance strategies and fine-tuning control parameters without
risking physical damage to the robot.

Challenges:

  * •

Complex Integration: Integrating AI, ML, and DTT can be complex and requires
expertise in multiple domains.

  * •

Data Handling: Managing vast amounts of data generated by sensors and digital
twins can be challenging. Efficient data storage, processing, and analysis are
necessary.

  * •

Training and Calibration: Training AI and ML models, as well as calibrating
digital twins, requires time and effort. The accuracy of these models depends
on the quality of data and the realism of the digital twin.

  * •

Cost: Implementing and maintaining integrated systems can be expensive.
Organizations must weigh the costs against the benefits of improved fault
tolerance and performance.

  * •

Cybersecurity: Protecting the digital twin and the associated AI and ML
systems from cyber threats is crucial. The integration of these technologies
can introduce vulnerabilities that need to be addressed.

##  VIII ROBOTIC MANIPULATOR APPLICATION - USE CASES

In this section, we will delve into detailed use case application that will
exemplify real-world applications of FTC for robotic manipulators. These case
studies highlight the pivotal role of AI, ML, and DT technologies in enhancing
FDD/FTC.

  1. 1.

Automotive Manufacturing: In an automotive manufacturing facility, robotic
manipulators are responsible for tasks like welding and painting. A digital
twin, equipped with AI and ML algorithms, continuously monitors the
performance of these robots. AI is utilized for predictive maintenance,
analyzing historical data and real-time sensor information to predict when
components are likely to fail. ML, integrated with the DT, enables anomaly
detection by learning normal robot behavior.

By leveraging AI, ML, and DT technologies, the automotive manufacturer
achieved a significant reduction in unplanned downtime. Predictive maintenance
allowed them to replace components before they failed, minimizing production
interruptions. Anomaly detection led to early identification of issues,
preventing defects in the final product. The integrated approach resulted in
cost savings and improved product quality.

Implementing predictive maintenance through digital twins requires a robust
data infrastructure to collect and analyze vast amounts of sensor data.
Additionally, the models need to be continuously updated to adapt to changing
conditions and data patterns.

  2. 2.

Medical Robotics: In the field of medical robotics, DT play a vital role in
surgical procedures. A DT combined with AI and ML, constantly monitors the
robot’s performance during surgery. AI algorithms optimize the robotic
system’s movements in real time to enhance precision and safety. The digital
twin simulates various fault scenarios to allow the robot to practice fault
recovery strategies.

The integration of AI, ML, and DT in medical robotics significantly improved
the precision and safety of surgical procedures. The real-time optimization of
robot movements reduced the risk of errors. The simulation of fault scenarios
enabled the robot to respond effectively to unexpected events during surgery,
ensuring patient safety.

The implementation of AI and ML in real-time surgical robotics requires
rigorous testing and validation to ensure patient safety. It is essential to
simulate a wide range of fault scenarios to prepare the robotic system for
unexpected events during surgery.

  3. 3.

Space Exploration: In space exploration missions, robotic manipulators are
exposed to extreme environmental conditions. Digital twin technology, enhanced
with AI and ML, continuously monitors the robot’s performance. AI is used to
predict potential faults based on environmental data, while ML models are
trained to adapt the robot’s behavior to changing conditions through RL. The
DT simulates fault scenarios, enabling the robot to practice fault recovery
strategies.

The integration of AI, ML, and DT technologies in space robotics significantly
increased the reliability and adaptability of robotic systems. Predictive
maintenance allowed for early intervention to prevent failures in the harsh
space environment. The autonomous fault recovery strategies, learned through
RL, ensured the robot could continue its mission even in the presence of
unforeseen challenges.

Space exploration requires robust FTC systems, and the integration of AI, ML,
and DT technologies must be rigorously tested to handle the unique conditions
of space missions. Regular updates to the models and simulations are essential
to adapt to the dynamic space environment.

  4. 4.

Industrial Logistics: In industrial logistics, robotic manipulators are used
for tasks like sorting and packaging. A digital twin, coupled with AI and ML,
continuously monitors the performance of these robots. AI-driven predictive
maintenance ensures that components are replaced before they fail. ML
algorithms integrated with the DT are responsible for detecting anomalies in
robot behavior.

The implementation of AI, ML, and DT in industrial logistics led to a
significant reduction in operational disruptions. Predictive maintenance
prevented unexpected failures, reducing downtime in critical sorting and
packaging processes. Anomaly detection ensured that defective products were
identified early, preserving product quality and customer satisfaction.

Real-time data analysis and proactive maintenance through DT require robust
infrastructure and ongoing model calibration. The integration of AI and ML
must consider the unique requirements and operational conditions of industrial
logistics.

  5. 5.

Agriculture Automation: In agriculture automation, robotic manipulators are
employed for tasks like planting and harvesting. A DT, enhanced with AI and
ML, continuously assesses the robot’s performance. AI-driven predictive
maintenance analyzes historical data and real-time sensor information to
predict when components may fail. ML algorithms integrated with the digital
twin enable anomaly detection by learning normal robot behavior.

The integration of AI, ML, and DT in agriculture automation resulted in
increased efficiency and reduced maintenance costs. Predictive maintenance
allowed for timely component replacements, minimizing disruptions during
planting and harvesting seasons. Anomaly detection identified issues early,
preventing crop damage and improving yield.

Implementing predictive maintenance in agriculture automation requires
consideration of environmental factors and seasonal variations. Models must be
adaptable to changing conditions and calibrated accordingly.

These use cases illustrate the significant impact of integrating AI, ML, and
DT technologies in FTC for robotic manipulators across diverse industries. The
outcomes demonstrate improved fault tolerance, reduced downtime, increased
operational efficiency, and enhanced product quality. The lessons learned
emphasize the importance of robust data infrastructure, rigorous testing, and
adaptability to dynamic conditions when implementing these technologies in FTC
systems.

##  IX Challenges and Future Directions

As the field of FTC for robotic manipulators continues to evolve, it faces a
set of challenges and opportunities that shape its trajectory. In this
section, we will examine the current challenges, discuss potential solutions
and strategies, and outline emerging trends and future directions.

###  IX-A Current Challenges in Implementing FTC for Robotic Manipulators

  1. 1.

Complexity of Robotic Systems: Modern robotic manipulators are increasingly
complex, with numerous components and sensors. Managing and monitoring these
systems in real time poses significant challenges, as it demands robust fault
detection, diagnosis, and recovery mechanisms.

  2. 2.

Data Management: The vast amount of data generated by sensors and digital
twins can overwhelm traditional data storage and processing capabilities.
Efficient data management and analysis are essential for effective FTC
implementation.

  3. 3.

Modeling Accuracy: Developing accurate models for digital twins is a critical
aspect of FTC. Modeling errors can lead to false alarms or undetected faults.
Achieving high-fidelity models that represent the dynamics and behavior of
robotic manipulators remains a challenge.

  4. 4.

Integration of Multiple Technologies: Integrating AI, ML, DT technologies
requires a multidisciplinary approach. Ensuring seamless communication and
compatibility among these technologies can be complex and challenging.

  5. 5.

Cybersecurity: The integration of DT and AI introduces cybersecurity concerns.
Protecting the digital twin and associated AI systems from cyber threats is
crucial to prevent unauthorized access and tampering with control algorithms.

###  IX-B Potential Solutions and Strategies to Address Challenges

  1. 1.

Advanced Data Analytics: Implementing advanced data analytics, including big
data and ML techniques, can assist in handling the vast amount of data
generated by sensors. This can improve fault detection, diagnosis, and
predictive maintenance.

  2. 2.

Improved Sensor Technology: The development of more advanced and reliable
sensor technology can enhance the quality and quantity of data available for
FTC. High-precision sensors with redundancy options can contribute to robust
fault detection and diagnosis.

  3. 3.

Modeling Advancements: Continued research into modeling techniques can lead to
more accurate digital twins. Utilizing physics-based models in conjunction
with data-driven approaches can improve the prediction and diagnosis of
faults.

  4. 4.

Standardization: Developing industry standards for the integration of AI, ML,
and DT technologies in FTC can ensure compatibility and interoperability
between different systems and components.

  5. 5.

Education and Training: Providing training and education to engineers and
practitioners in the field of FTC is essential. Building expertise in AI, ML,
and DT technologies will facilitate the implementation of these tools in
practice.

###  IX-C Emerging Trends and Future Directions

  1. 1.

Explainable AI (XAI): As AI plays a more prominent role in FTC, the need for
XAI becomes critical. Developing AI systems that can explain their reasoning
and decision-making processes will enhance transparency and trust in fault
tolerance mechanisms.

  2. 2.

Edge Computing: Implementing edge computing in robotic manipulators can reduce
latency and enhance real-time decision-making. AI and ML models can be
deployed on the robot itself, allowing for faster fault detection and control
adaptation.

  3. 3.

Quantum Computing: Quantum computing has the potential to revolutionize fault
tolerance by rapidly solving complex optimization and decision-making
problems. As quantum computing technology matures, it may find applications in
FTC for robotic manipulators.

  4. 4.

Distributed and Multi-Agent Systems: The use of distributed and multi-agent
systems for robotic manipulators can improve fault tolerance. Multiple robots
can collaborate and adapt to faults in a coordinated manner, ensuring
uninterrupted operation.

  5. 5.

Human-Machine Collaboration: Integrating human expertise with AI systems is an
emerging trend. Human operators can work in collaboration with AI-powered
control systems, providing insights and decision-making in complex fault
scenarios.

  6. 6.

Adaptive Learning and Reinforcement Learning: Advancements in adaptive
learning techniques and reinforcement learning algorithms can lead to more
efficient and adaptive fault recovery strategies. These technologies will be
critical for achieving autonomy in fault-tolerant robotic systems.

  7. 7.

Cross-Domain Transferability: Transferring knowledge and strategies from one
domain to another is an emerging trend. Lessons learned from fault tolerance
in one industry, such as manufacturing, can be applied to another, like
healthcare robotics, with appropriate adaptations.

  8. 8.

Sustainability and Energy Efficiency: Future directions in FTC will also
consider sustainability and energy efficiency. Optimizing control strategies
to minimize energy consumption while maintaining fault tolerance is of growing
importance.

In summary, the challenges, and opportunities in the field of FTC for robotic
manipulators are closely intertwined. Addressing the current challenges
requires a combination of advanced technologies, interdisciplinary
collaboration, and a focus on education and training. The emerging trends and
future directions highlight the continued evolution of fault-tolerance
control, with a growing emphasis on transparency, autonomy, and adaptability
in robotic systems. As the field progresses, it will play an increasingly
integral role in ensuring the reliability, safety, and performance of robotic
manipulators across various industries.

##  X Conclusion

This article has provided a comprehensive exploration of FTC for robotic
manipulators, shedding light on the remarkable advancements driven by AI, ML
and DT technologies. These technologies have ushered in a new era of fault
tolerance, offering enhanced reliability, safety, and adaptability to robotic
systems across various industries. Let’s summarize the key takeaways from this
discussion.

First and foremost, FTC plays a pivotal role in ensuring the uninterrupted
operation of robotic manipulators. The ability to detect, diagnose, and
recover from faults is essential in industries where precision, efficiency,
and safety are paramount. Whether in automotive manufacturing, healthcare
robotics, space exploration, industrial logistics, or agriculture automation,
FTC’s impact is profound.

The integration of AI, ML, and DT technologies is a game-changer in the field
of FTC. AI-powered DT, combined withML, provide real-time monitoring,
predictive maintenance, and anomaly detection capabilities. These technologies
enable robotic manipulators to adapt to changing conditions, recover from
faults autonomously, and reduce unplanned downtime. The result is not only
cost savings but also improved product quality and operational efficiency.

The use cases presented in this article demonstrate the real-world
applications of these integrated technologies. From automotive manufacturing
to space exploration, the outcomes are clear: enhanced fault tolerance,
reduced downtime, and improved safety. These examples also underscore the
importance of proactive maintenance, precise fault detection, and autonomous
recovery strategies.

Looking ahead, the field of FTC is poised for continued growth and
significance. Emerging trends such as Explainable AI (XAI), edge computing,
quantum computing, and human-machine collaboration promise to further enhance
fault tolerance in robotic systems. Cross-domain transferability of knowledge
and strategies will allow industries to benefit from one another’s
experiences.

Furthermore, the emphasis on sustainability and energy efficiency aligns with
the broader goals of responsible technological development. Optimizing control
strategies to minimize energy consumption while maintaining fault tolerance is
crucial, especially in an era of increasing environmental consciousness.

In Conclusion, the synergy between AI, ML, and DT technologies is a
revolutionary transformation in FDD/FTC for robotic manipulators. The
continued integration of these technologies, coupled with ongoing research and
innovation, will shape the future of robotics, ensuring that these systems not
only perform with exceptional precision but also do so with unparalleled
reliability, adaptability, and resilience in the face of unexpected
challenges. As industries continue to rely on robotic manipulators for various
applications, the growth and significance of FDD/FTC will remain at the
forefront of technological progress.

|  Md Muzakkir Quamar, is currently pursuing his PhD in controls and
Instrumentation Engineering Department at KFUPM. He obtained his MS degree in
controls and Instrumentation Engineering from KFUPM in 2022. He is the
recipient of full-time scholarship for MS and PhD at KFUPM. His research
Interest include nonlinear system, robotics, multi-agent system, optimal and
robust control.
---|---
|  Ali Nasir, is currently working as an assistant professor in the Control
and Instrumentation Engineering Department at KFUPM. He is an affiliate of the
Interdisciplinary Research Center for Intelligent Manufacturing and Robotics
at KFUPM. He is also a guest affiliate of the Interdisciplinary Research
Center for Aviation and Space Exploration. He obtained his PhD degree in
Aerospace Engineering along with M.Sc. degrees in Electrical Engineering and
Aerospace Engineering from the University of Michigan, Ann Arbor, USA. He
received his B.Sc. in Electrical Engineering from University of Engineering
and Technology, Taxila. He is the recipient of Fulbright scholarship for an MS
leading to a PhD. He is currently working on fault-tolerant control and
decision-making for industrial robots. His research interests also include
approximate dynamic programming, nonlinear control, and estimation.
---|---

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