滤波减速器传动机器人的自适应RBF反演法控制

针对具有参数未知、外界扰动、强耦合、非线性和多变量的滤波减速器传动机器人建立系统数学模型并对其进行自适应RBF神经网络反演法控制。利用自适应RBF在线逼近系统模型中的未知非线性项设计基于自适应RBF神经网络的反演法控制器同时结合Lyapunov稳定性分析方法论证闭环系统的收敛性。所提控制方法有效地抑制诸如参数未知、外界扰动等对滤波减速器传动机器人的性能影响。仿真分析表明所提出自适应RBF神经网络反演控制器实现了滤波减速器传动机器人的高性能位置跟踪控制并具有很好的控制精度和鲁棒性。

Adaptive RBF networks backstepping control of robots with filtering reducer transmission

A mathematic model for filtering reducer transmission robots which have the characteristics of parameter uncertaintiesexternal disturbancestrong couplingnonlinearities and multi-variable is establishedand then an adaptive RBF networks backstepping method is proposed to control the robot.RBF is employed to approach the nonlinear item of the filtering reducer transmission robot on lineand an adaptive RBF network controller is developed based on backstepping method which effectively suppresses the effect of parameter uncertainties and external disturbance etc.It is combined with Lyapunov stabilization analytical method to demonstrate the close loop system convergence.Simulation results verify that the proposed controller not only satisfies high performance position trackingbut also has good accuracy and robustness.