Self-adaptive Composite Control for Flexible Joint Robot Based on RBF Neural Network
Among numerous control schemes for flexible joint robots, the main problem is that the full state variable of acceleration and jerk must be known, which are difficult to measure, and the noise may be merged in the main signal. To solve this problem, a self adaptive composite control scheme is developed to control the flexible joint robots with modeling errors and subject to uncertain disturbances, which is based on considering the system as a low dimensional nominal rigid and a linear elastic subsystem. Using this approach, the controller consists of a slow and a fast term, the slaw control is based on the weB-known Comp~ed Torque method and a RBF neural network based compensating controller. The neural network is trained on line based on Lyapunov theory to compensate for the modeling uncertainties, thus its convergence is guaranteed. The fast term is designed to provide stiffness and damping for eliminating elastic deformation. Simulations are presented for a planner manipulator with two flexible joints, the trajectory tracking results are provided to demonstrate performance of the scheme.
flexible joint self-adaptive composite control neural network trajectory tracking
LI Xin ZHU Yu YANG Kai-ming
Department of Precision Instruments and Meehanology, Tsinghua University State Key Laboratory of Tribology, Tsinghua University Beijing, China
国际会议
厦门
英文
837-840
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）