Adaptive Neural Network Compensating Control for Coordinated Motion of Space-Based Robot System With Uncertain Parameters and External Disturbances

The control problem of space-based robot system with uncertain parameters and external disturbances is considered. With Lagrangian formulation and augmentation approach, the dynamic equations of space-based robot system in workspace are derived. Based on the results, an adaptive neural network compensating control scheme for coordinated motion between the base’s attitude and end-effector of space-based robot system is developed. It is based on the inertia-related method, and incorporates a neural network controller to compensate the uncertainties. The closed-loop system stability with the neural network adapted on-line is discussed in detail through the Lyapunov stability approach. Comparing with many adaptive and robust control schemes, the controller proposed does not require one to determine the regression matrix for space robot system and then avoids tedious computations. Numerical simulations are provided to show the effectiveness of the approach.