Robust chattering-free sliding mode control of space robot in task space  

作　　者：Baomin FENG Guangcheng MA Qiyong WEN Changhong WANG 

机构地区：[1]Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin Heilongjiang 150001, China

出　　处：《控制理论与应用（英文版）》2008年第2期146-152,共7页

基　　金：the National High-Tech Research & Development Program, China

摘　　要：This paper studies the tracking control problem of a free-floating space robot in a task space. Considering the model uncertainties and external disturbance, a robust sliding mode controller is proposed using the Lyapunov direct method and dissipative theory. To eliminate the chattering phenomenon, an radial basis function （RBF） neural network is applied to replace the discontinuous part of the control signal. A novel on-line learning method of the weights and parameters of the RBF neural network established using Lyapunov function assures the stability of the system. It is proved that the proposed controller can guarantee that the L2 gain from disturbance to tracking error is lower than the given index y. Simulation results show that the control method is valid.This paper studies the tracking control problem of a free-floating space robot in a task space. Considering the model uncertainties and external disturbance, a robust sliding mode controller is proposed using the Lyapunov direct method and dissipative theory. To eliminate the chattering phenomenon, an radial basis function （RBF） neural network is applied to replace the discontinuous part of the control signal. A novel on-line learning method of the weights and parameters of the RBF neural network established using Lyapunov function assures the stability of the system. It is proved that the proposed controller can guarantee that the L2 gain from disturbance to tracking error is lower than the given index y. Simulation results show that the control method is valid.

关 键 词：Space robot Tracking control Sliding mode control Neural network 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]