  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.
