Adaptive Neural Learning Control of Rigid-link Electrically-driven Robot Manipulators

摘要：

Based on deterministic learning theory which states that an appropriately designed adaptive neural controller can learn the unknown system internal dynamics during a stable control process,this paper investigates deterministic learning from adaptive neural control of rigid-link electrically-driven (RLED) robot manipulators with completely unknown system dynamics. Firstly,the recent results on localized RBF networks and stability analysis of linear time-varying (LTV) systems are presented. Secondly,a stable adaptive neural control algorithm is designed for RLED robot manipulators,and the closed-loop control system with the LTV form is obtained. Deterministic learning of RLED robot manipulators is analyzed,locally-accurate approximation of the closed-loop control system dynamics is achieved along the periodic tracking orbit. Improved control performance is achieved using learned knowledge stored as a set of constant neural weights. Finally,simulation example is presented to demonstrate the effectiveness of the proposed control algorithm

作者: WU Yuxiang HE Qizhen WANG Cong

作者单位: South China University of Technology,Guangzhou 510641,P.R.China

会议类型: 国际会议

会议名称: The 30th Chinese Control Conference(第三十届中国控制会议)

会议地点: 烟台

会议语种:英文

页码: 1-7

在线出版日期: 2011-07-01（万方平台首次上网日期，不代表论文的发表时间）