Kalman-filter-based robust control for hypersonic flight vehicle with measurement noises

This paper investigates the tracking control problem of hypersonic flight vehicle (HFV) with measurement noises and system uncertainties. The system states of HFV are usually difficult to be accurately measured due to sensor noises and complex flight environment, which makes the controller design extremely difficult. To deal with such a problem, a linear quadratic Gaussian (LQG) optimal control algorithm and a robust back-stepping control strategy are proposed using Kalman filter. Firstly, the Kalman filter is used as a state observer for HFV to estimate the true states polluted by noises. Then, a linear LQG controller is proposed to realize the tracking control with the estimated states. In addition, to deal with the more practical case that the system suffers from both measurement noises and system uncertainties, a robust back-stepping control scheme based on Kalman filter and disturbance observer is presented. Different from the existing results, the proposed control scheme can guarantee the tracking performance under measurement noises and uncertainties. Finally, simulation results of HFV are carried out to demonstrate the effectiveness of the two proposed controllers.