基于RBF神经网络的柔性机械臂位置控制

针对复杂的柔性机械臂位置控制问题,提出一种结合极点配置技术的自适应滑模控制方法。变结构滑模应用于柔性臂的刚性运动和弹性振动抑制的控制,极点配置用以设置滑模面的极点,以获得良好的动态响应特性。利用RBF网络自适应性学习系统不确定量的上界,神经网络的输出用于自适应修正控制律的切换增益。实例仿真结果表明,该控制方法能在对机械臂位置控制的同时有效地抑制柔性臂的弹性振动,对不确定参数具有鲁棒性。

Flexible Manipulator Position Control Based on RBF Neural Network

In view of the complicated flexible manipulator position control problem, puts forward an adaptive sliding mode control method, which combined with pole assignment technology. The variable structure sliding mode control is applied to the rigid motion and elastic vibration suppression of flexible manipulator and the pole assignment is used to set the sliding mode surface pole to obtain good dynamic response. By means of RBF network adaptive learning system upper bound of the uncertainties, the neural network output is used for adaptive correcting the switch gain of control law. Instance simulation results show that the proposed scheme is capable of manipulator position control, and meanwhile it can effectively restrain the elastic vibration of flexible arm and is robust to uncertain parameters.