Incremental Nonlinear Reinforcement Learning Control for a Civil Aircraft with Model Uncertainties and Actuator Faults

The problem of fault-tolerant attitude tracking control for the civil aircraft with model uncertainties and actuator faults is studied. A robust multiple inversion-based incremental nonlinear dynamic inversion (RMI-INDI) fault-tolerant control method is proposed for the problem. Firstly, considering that the higher-order term is neglected in the INDI method, an RMI method is proposed to deal with the higher-order term and model uncertainties of the INDI control. Secondly, to achieve the optimal control parameters for the INDI controller, a reinforcement learning (RL) method is suggested, where a Deep Deterministic Policy Gradient (DDPG) algorithm with a smooth reward function is designed. Finally, performances of the proposed RL-RMI-INDI fault-tolerant controller are demonstrated by using two scenario simulations. Compared with the SMC control, RMI-NDI control and INDI control without RL, tracking errors and overshoots are greatly reduced by the proposed RL-RMI-INDI controller for attitude tracking missions, even under model uncertainties and actuator faults.