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

arXiv: https://arxiv.org/abs/2402.02980 · HTML: https://mlsanity.com/paper/2402.02980/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 305 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [Context Autoencoder for Self-Supervised Representation Learning](https://mlsanity.com/paper/2202.03026/) (2022)
- [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018)
- [Survey on Deep Neural Networks in Speech and Vision Systems](https://mlsanity.com/paper/1908.07656/) (2019)
- [Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review](https://mlsanity.com/paper/2102.04148/) (2021)
- [Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery](https://mlsanity.com/paper/2101.01625/) (2021)
- [Latent Space Data Assimilation by using Deep Learning](https://mlsanity.com/paper/2104.00430/) (2021)
- [The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems](https://mlsanity.com/paper/2105.04408/) (2021)
- [Fault-tolerant Control of Robot Manipulators with Sensory Faults using Unbiased Active Inference](https://mlsanity.com/paper/2104.01817/) (2021)
- [Distributed Adaptive Fault-Tolerant Control of Uncertain Multi-Agent Systems](https://mlsanity.com/paper/1505.05537/) (2015)
- [Sensor Fault Detection and Compensation with Performance Prescription for Robotic Manipulators](https://mlsanity.com/paper/2305.19157/) (2023)

## Built on by (most cited first, 2 of 2)

- [Vision-Based Early Fault Diagnosis and Self-Recovery for Strawberry Harvesting Robots](https://mlsanity.com/paper/2601.02085/) (2026)
- [Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels](https://mlsanity.com/paper/2607.26121/) (2026)

## Abstract (arXiv)

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.