柔性和摩擦力不确定条件下RBF神经网络自适应轨迹跟踪方法

RBF Neural Network Adaptive Trajectory Tracking Control under Conditions of Uncertain Compliance and Friction

提出一种基于径向基神经网络(Radial basis function,RBF)的力/位置混合自适应控制方法并用于机器人轨迹跟踪控制,解决机器人柔性末端执行器轨迹跟踪过程中柔性和摩擦力模型难以精确描述的问题。RBF神经网络是一种高效的前馈式神经网络,具有其他前向网络所不具有的非线性逼近性能和全局最优特性,并且网络结构简单,训练速度快。设计一种基于RBF神经网络非线性逼近能力来估计模型中的不确定参数的自适应控制器,给出控制器中神经网络权值更新规则,并证明所设计控制器输出力和位置误差的最终一致有界性。将该控制器应用于风管清扫机器人仿真试验,结果表明该自适应控制器能很好地用于柔性和摩擦力不确定条件下轨迹跟踪控制,与传统自适应控制方法相比具有更精确的跟踪特性和更强的鲁棒性。

A force/position hybrid adaptive control method based on radial basis function（RBF） neural network is proposed to solve the problem of difficulties to precisely describe the compliance and friction for robot terminal during trajectory tracking process.RBF neural network is an efficient feed-forward neural network with non-linear approximation and global optimization characteristics,which is not provided by other feed-forward networks,which is simple in network structure,and rapid in training speed.An adaptive controller is designed that relies on the nonlinear approximation ability of the RBF neural network to estimate the uncertainty factors in the models,the update rules for the weights of the controller neural network is provided and its finally uniform boundedness of the errors of the controller output force and position is proved.The controller is applied to a duct cleaning robot for simulation experiments.Simulation results shows that the adaptive controller demonstrates superior tracking precision and robustness compared with traditional adaptive controller.