Neural adaptive control for a ground experiment of the space proximity operation in a six-degree-of-freedom micro-gravity simulation system

In this study, a neural adaptive controller is developed for a ground experiment with a spacecraft proximity operation. As the water resistance in the experiment is highly nonlinear and can significantly affect the fidelity of the ground experiment, the water resistance must be estimated accurately and compensated using an active force online. For this problem, a novel control algorithm combined with Chebyshev Neural Networks (CNN) and an Active Disturbance Rejection Control (ADRC) is proposed. Specifically, the CNN algorithm is used to estimate the water resistance. The advantage of the CNN estimation is that the coefficients of the approximation can be adaptively changed to minimize the estimation error. Combined with the ADRC algorithm, the total disturbance is compensated in the experiment to improve the fidelity. The dynamic model of the spacecraft proximity maneuver in the experiment is established. The ground experiment of the proximity maneuver that considers an obstacle is provided to verify the efficiency of the proposed controller. The results demonstrate that the proposed method outperforms the pure ADRC method and can achieve close-to-real-time performance for the spacecraft proximity maneuver.