可重构机械臂构型自适应控制研究

Configuration Adaptive Control of Reconfigurable Robot Arm Based on RBF Neural Network

针对可重构机械臂系统存在的不确定性及不同构型下的轨迹跟踪问题,提出了径向基函数(Radial Basis Function,RBF)神经网络鲁棒自适应补偿控制算法。设计了RBF神经网络补偿控制器自适应逼近补偿系统存在的未知项;为减小控制器逼近误差及适应构型变化时的鲁棒性,在控制律中引入了鲁棒项;基于李雅普诺夫(Lyapunov)稳定性理论设计了构型自适应调节律和鲁棒项并证明了闭环控制系统的稳定性。最后,以两种典型的可重构机械臂构型进行研究,结果表明所提算法能够适应系统构型的改变,同时有效地补偿系统存在的不确定性。

For solving the uncertainties problems of the reconfigurable robot arm and the trajectory tracking problems of the robot configuration changes, a new robust configuration-adaptive control strategy based on RBF neural network is presented. RBF neural network compensating controller is designed to approximate the uncertain items including the friction, the parameter perturbations and the external disturbances. In order to decrease the system approximation error and improve the robustness of the changes of configuration,the robust term is appended to the above controller. Based on the Lyapunov stability theory, an configuration-adaptive parameter adjustment law plus a robust term is also proposed, and the system stability is verified subsequently. Finally, taken the two typical configurations of the reconfigurable robot arm for example, the substantial experiential results show that the proposed controller can quickly adapt to the configuration changes and effectively compensate the system uncertainties, similarly.