Sliding Mode Control for Hypersonic Vehicle Based on Extreme Learning Machine Neural Network Disturbance Observer

Abstract

The novel extreme learning machine (ELM) neural network disturbance observer (NNDO) -based sliding mode control (SMC) strategy is proposed for the precise tracking control of a hypersonic vehicle (HV) under various disturbance situations. By converting nonlinear dynamics into state-dependent linear model, the control law design process is simplified, and the sliding mode control law based on the power function reaching rate is designed to suppress the chattering effect. Considering the disturbances, the ELM-NNDO is designed based on the single-hidden layer feedforward network (SLFN). Different from conventional ELM using least square optimization approach, the output weight here is updated based on the Lyapunov synthesis approach. In addition, the influences of the disturbances on the velocity and altitude are attenuated by the direct feedback compensation (DFC), and the offset-free tracking control is realized for the output reference signal. Comparison of simulation results verify the superior control performance of the proposed method.