Fault Tolerant Control Using Reinforcement Learning and Particle Swarm Optimization

Diversity, uncertainty and suddenness of unexpected faults bring a challenge for fault tolerant control due to the lack of valid data especially for a fault during an early stage. In this study, a reinforcement learning approach with a critic action architecture is proposed to overcome this challenge by designing an online learning fault-tolerant controller so that the faulty system can approximate the performance index of the fault-free system. Different from the traditional Hebb enhancement rules in the reinforcement learning, the training process is speeded up by introducing a supervisory learning on the basis of the training dataset which is built with the states and the virtual optimal control acquired by particle swarm optimization. The effectiveness of the algorithm is demonstrated by a test bed of a three-tank system.