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.

翻译：这篇综述性文章深入探讨了针对机器人操纵器定制的容错控制方案。我们的研究涵盖容错控制的历史演变，追溯其随时间的发展历程，并细致审视了由人工智能、机器学习和数字孪生技术等尖端技术的协同融合所驱动的最新突破。本文特别强调了这些当代趋势对机器人操纵器控制与容错领域产生的变革性影响。通过深入历史背景，我们旨在提供对容错控制方案演变的全面理解。这一历程涵盖了从基于模型和基于信号的方案到传感器作用的转变，为探索当下由人工智能、机器学习和数字孪生技术实现的范式转变奠定了基础。叙事随着我们剖析这些先进技术与其在增强机器人操纵器领域容错性中应用之间的复杂相互作用而展开。我们的综述批判性地评估了这些进展的影响，揭示了近年来出现的新方法、技术及应用。本文的总体目标是呈现关于机器人操纵器背景下故障诊断与容错控制当前状态的全面视角，将我们的研究置于人工智能、机器学习和数字孪生技术进步的更广阔框架内。通过对历史基础与当代创新的细致审视，这篇综述显著充实了现有知识体系，为在机器人操纵器控制这一动态领域探索的研究人员、从业者和爱好者提供了宝贵的洞见。