Back-stepping control of free-floating space robot based on adaptive neural network

Abstract

Trajectory tracking control problems of the free-floating space robot are considered by the paper, back-stepping control method based on adaptive neural network is put forward. The complex system is decomposed into several simple sub-systems. The control laws are designed by derived, so that closed-loop stability can be obtained by each subsystem; Because of the influence of interference and the measurement level limitation, accurate mathematical model is difficult to be obtained. Neural network controller of good nonlinear approximation ability is designed to compensate the uncertainty of system model. Adaptive learning laws are designed to ensure that weights can be adjusted online real-time. The system uniformly ultimately bounded (UUB) is proved based on the Lyapunov theory. Simulation experiments show that the control method can fast track the desired trajectory, and has a good application value for space robotic manipulators with uncertainty.