自由漂浮双臂空间机器人基于RBF神经元网络的关节运动自适应控制  

Adaptive Control of Joint Motion of Free-Floating Space Robot System with Dual-Arms Based on RBF Neural Network

作　　者：郭益深[1] 陈力[1] 

机构地区：[1]福州大学机械工程及自动化学院,福州350002

出　　处：《系统仿真学报》2009年第10期3051-3055,3061,共6页Journal of System Simulation

基　　金：国家自然科学基金(10372022;10672040);福建省自然科学基金(E0410008)

摘　　要：讨论了自由漂浮双臂空间机器人关节运动的控制问题。由拉格朗日第二类方法及系统动量、动量矩守恒关系,建立了自由漂浮双臂空间机器人完全能控形式的系统动力学方程。以此为基础,借助于RBF神经网络技术、Ge-Lee(GL)矩阵及其乘积算子定义,对自由漂浮双臂空间机器人进行了神经网络系统建模;之后针对双臂空间机器人系统所有惯性参数均未知的情况,设计了自由漂浮双臂空间机器人基于RBF神经元网络的关节运动自适应控制方案。提出的控制方案不要求系统动力学方程具有惯常的关于惯性参数的线性性质,且无需预知系统惯性参数的任何信息,也无需对神经网络进行离线训练、学习,所以更适于实时、在线应用。系统数值仿真证实了该控制方案的有效性。The control problem of joint motion of a free-floating space robot with dual-arms was discussed. With the Lagrangian approach and the linear, angular momentum conversation, the full-controlled dynamic equations of the free-floating space robot with dual-arms were derived. Based on the above results, the free-floating space robot with dual-arms was modeled by the RBF neural network technique, the Ge-Lee （GL） matrix and its product operator. With all unknown inertial parameters, the adaptive control scheme of joint motion of the free-floating space robot was developed based on RBF neural network. This proposed control scheme needs neither linearly parameterize the dynamic equations of the system and knows any actual inertial parameters, nor trains the neural network offline so that it could be prone to realtime and online application. The simulation results verify the feasibility of the proposed control scheme.

关 键 词：自由漂浮 双臂空间机器人 RBF神经元网络 GL矩阵及其乘积算子 关节运动 自适应控制 

分 类 号：TP241[自动化与计算机技术—检测技术与自动化装置]