High-Order Disturbance Observer-Based Neural Adaptive Control for Space Unmanned Systems with Stochastic and High-dynamic Uncertainties

In this paper, a high order disturbance observer based stochastic adaptive anti-disturbance control algorithm has been designed for the space unmanned systems (SUSs) with high dynamic disturbances and stochastic uncertainties. Firstly, to suppress the adverse influence of the high dynamic disturbances, a high order disturbance observer is designed for the SUSs to maintain the accurate approximation. Secondly, to overcome the infaust effects of the stochastic uncertainties, a novel variable has been introduced and the corresponding adaptive law has been proposed. Moreover, the neural networks have been employed to enhance the adaptability with respect to the nonlinearities and modeling errors. Based on the stochastic control theory and the fourth-order Lyapunov function, the stochastic stability of the closed-loop control system has been proved. Finally, the performance of high-order disturbance observer has been verified in two cases of simulations, and the effectiveness of the stochastic adaptive anti-disturbance control strategy has been demonstrated simultaneously.