基于神经网络的柔性机械手Backstepping控制

Backstepping Controller for Flexible Manipulator Based on Neural Network

考虑到柔性机械手动力学方程和高度非线性等特点,在控制器设计中引入Backstepping设计思想,将原复杂的高阶非线性系统分解成低阶简单系统。为了有效的逼近被控对象中非线性函数,本文提出一种将径向基函数(Radial basis function,RBF)神经网络与Backstepping控制思想相结合的控制方法。仿真结果表明,基于径向基函数的神经网络Backstepping控制方法能够实现柔性机械手控制系统的稳定。

The dynamic equations of the flexible manipulator are complex and highly nonlinear,the Backstepping design idea is introduced into the controller design and the original complex high-order nonlinear system is decomposed into a low-order simple system.In order to effectively approximate the nonlinear function in the controlled object,it proposes a control method that combines the Radial basis function(RBF)neural network with the backstepping control idea.The simulation results show that the neural network backstepping control method based on radial basis function can realize the stability of the flexible manipulator control system.