Reinforcement Learning-Based Fault Tolerant Control Design for Aero-Engines With Multiple Types of Faults

In this paper, a reinforcement learning (RL) based fault tolerant control (FTC) strategy is investigated for the bleed air temperature control system (BATCS) of an aero-engine with time delays, parameter uncertainties and multiple types of faults. By the design of offline training networks, the deep deterministic policy gradient (DDPG) algorithm is employed to update the parameters of training networks. Then a data driven FTC input combined with exponential weighted moving average (EWMA) filter can be obtained to achieve the fault tolerant tracking control of BATCS. Finally, a comparison simulation between PID and RL based FTC scheme is demonstrated to verify the feasibility of the research.