In response to the issue of total interference suppression in space flexible manipulator systems, a state estimation and dynamic compensation cooperative sliding mode control strategy is proposed, based on a neural network-based disturbance observer and a sliding mode observer. This approach leverages the approximation capability of neural networks to estimate and compensate for the system's composite uncertainties in real-time. By combining a nonsingular terminal sliding mode control law, it ensures that the system's state converges to the equilibrium point within a finite time, significantly improving the system's tracking accuracy and robustness against disturbances. Considering the practical scenario where the system's states may not be fully measurable, a sliding mode observer is designed to accurately reconstruct the system's unknown states. Based on these estimated values, a cooperative sliding mode controller is designed. This cooperative control scheme effectively addresses the control challenges when the system's states are partially unmeasurable. Simulation results show that the system maintains excellent dynamic performance and stability, even in the presence of nonlinear factors such as friction.
Wang et al. (Mon,) studied this question.