RBF Neural Network Adaptive Control for Space Robots without Speed Feedback Signal

Abstract

Tracking control problems for space robots are studied under conditions without speed feedback signals. An adaptive RBF neural network control method with a speed observer is proposed. Specially, we conduct the following. 1) A dynamic model of space robots is established. 2) A speed observer based on a neural network is designed to reconstruct speed information. 3) A controller based on a neural network is designed to compensate the nonlinear model of system. 4) A weight adaptive learning laws of the neural network is designed to ensure on-line tuning without an off-line learning phase. 5) The uniformly ultimately bounded state of the closed-loop system is proved based on Lyapunov theory. Simulation results show that the adaptive neural network control method with the speed observer can achieve good precision. This has important engineering value.