Actuator Fault

Actuator fault research focuses on enabling robots and autonomous systems to maintain functionality despite failures in their motors or other actuators. Current efforts concentrate on developing robust control strategies, often employing reinforcement learning algorithms or adaptive control methods, to compensate for these faults and maintain performance. This research is crucial for improving the reliability and safety of robots in various applications, from unmanned aerial vehicles to legged robots operating in challenging environments, by enabling continued operation even with degraded or failed components. Model-based and model-free approaches, including neural networks and Markov Decision Processes, are being explored to achieve fault detection, identification, and mitigation.