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

TL;DR

The paper addresses reliability and safety of robotic manipulators by surveying fault diagnosis and fault-tolerant control (FTC) across historical and contemporary developments. It emphasizes the integration of AI, ML, and Digital Twin Technology to improve fault detection, decision-making, and autonomous recovery. Key contributions include a unified synthesis of model-based and signal-based FTC, sensor roles, AI/DL/RL techniques, DT-enabled predictive maintenance, and cross-domain synergies. The review highlights real-world use cases across manufacturing, healthcare, space, logistics, and agriculture, and discusses challenges such as data management and cybersecurity. It provides guidance on future directions—explainable AI, edge computing, and hybrid physics-data models—to advance robust, autonomous FTC in robotic manipulators.

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.

Figures (6)

• Figure 1: Schematic block structure of FD and FTC.
• Figure 2: Simplified block structure of a robotic system kumar2023prognostics
• Figure 3: A typical robotic manipulator with 6-dof raouf2022mechanical
• Figure 4: Simplified architecture of Model based FTC img
• Figure 5: Simplified architecture of Signal-based FTC img