Active Fault-Tolerant Control Integrated with Reinforcement Learning Application to Robotic Manipulator

Abstract

In this paper, we propose an active fault-tolerant control framework for robotic manipulators subjected to joint actuator faults. The proposed active fault-tolerant control scheme includes a neural network-based fault diagnosis module and a reinforcement learning-based fault-tolerant control module. Once the actuator fault is detected and diagnosed, an additive reinforcement learning controller will produce compensation torques to guarantee the system safety and maintain the control performance. Compared to traditional methods, our strategy avoids overly relying on the exact system models, which has potential for wider applications. The scheme is evaluated on a 7-DOF Panda manipulator for tracking control tasks in the MuJoCo simulator. Simulation results demonstrate the effectiveness of the proposed framework in dealing with single and multiple actuator faults.