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Fault Detection and Optimization for Flight Vehicles via Deep Reinforcement Learning

The problem of fault detection for unmanned flight vehicle is investigated in the paper. Firstly, the linear switched model of flight vehicle is obtained via nonlinear dynamic model. The fault detection filter based on robust control theory is designed to generate the residual signal. To achieve better performance, deep reinforcement learning is applied to tune the parameters. The scheduling interval can be viewed as a neighborhood of parameters obtained by robust control theory. The stability of closed-loop system is guaranteed by the aid of average dwell time method and Lyapunov functional method. Simulation results in the end demonstrate the effectiveness and superiority of proposed method.