闭链双臂空间机器人抓持载荷基于径向基函数神经网络的补偿控制

Compensation Control for Grasped Object of Dual-arm Space Robot with Closed-chain Based on Radial Basis Function Neural Network

讨论具有闭合运动链的漂浮基双臂空间机器人抓持系统目标载荷的位置、力混合协调控制问题。采用多刚体动力学建模方法并结合漂浮基闭链抓持系统固有的运动学及动力学特性,获得抓持系统合成动力学方程。闭链双臂空间机器人系统参数极其复杂经常出现变动,针对抓持系统参数不确定的情况,根据神经网络控制理论和Lyapunov稳定性理论,设计该抓持系统基于径向基函数神经网络补偿的力/位置协调控制方案,从而达到对抓持负载位置与所受内力的双重控制效果。系统数值仿真证明了上述控制方案的准确性,仿真结果也证实了所提出的控制方案可有效地消除参数不确定对控制系统的影响。

Hybrid position and force control problem of grasped object of a free-floating space manipulator with closed kinematic chain is discussed.The dynamics model of synthetical system is obtained based on multi-bodies dynamics method and kinematic and dynamic characteristic of free-floating grasping systems,and the control problems for the object to track the desired trajectory in workspace and adjustment of interactive forces due to the interaction between the object and the end-effectors are discussed.Because of the high structure complexity and the parameter uncertainty of such systems,the scheme of radial basis function neural network compensation control with better robustness to uncertainty and disturbance is proposed to track the desired trajectory of the object,and the corresponded scheme of internal forces control is proposed synchronously.Therefore,the object position and internal forces can be regulated simultaneously.The effect of the controllers is testified by computer simulation and simulation results verify that the proposed control scheme can eliminate the effect of uncertain parameters on the control system.