柔性关节机器人的自适应神经网络动态面控制

摘要

A neural network based adaptive dynamic surface controller is proposed for uncertain flexible-joint robot systems. The dynamic surface control method eliminates the problem of“explosion of complexity”existing in traditional backstepping approach by the addition of low pass filters. RBF neural networks are used to approximate the unknown nonlinearities of the model. Nonlinear damping items are used to overcome the external disturbances. Adaptive laws are designed to estimate the weight values of the neural networks and unknown parameters. From Lyapunov stability analysis, it is shown that the control strategy can guarantee the semi-global stability of the closed-loop system and arbitrarily small tracking error by adjusting the controller parameters. Simulation results are presented to validate the good tracking performance of the control system.%针对带有不确定性的柔性关节机器人系统，提出一种自适应神经网络动态面控制。动态面控制方法通过引入一阶低通滤波器消除了传统backstepping方法存在的“微分爆炸”现象。构造RBF神经网络逼近系统模型的未知函数，利用非线性阻尼项克服外界干扰力矩，设计自适应律在线估计神经网络权值及模型未知参数。通过Lyapunov方法证明得出闭环系统所有信号半全局一致有界，跟踪误差可以通过调节控制器参数达到任意小。仿真结果证实了控制系统的良好跟踪效果。