定位平台非线性摩擦的神经网络补偿

提出了一种基于BP神经网络的机械伺服系统非线性摩擦的补偿方法，根据方法设计出一种将经典的PD控制与神经网络控制相结合的控制器，该控制器既有PD控制的优点，又能神经网络逼近非线性函数的能力，较好地补偿了系统中的非线性摩擦和外部扰动，应用Lyapunov稳定性定理，证明了系统的稳定性，并得到系统跟踪差的边界值，采用刚毛摩擦动力学模型，对X－Y定位平台进行仿真和实验，结果表明该控制器能够补偿系统的非线性因素，保证了系统的稳定，减少了跟踪误差，该方案控制效果明显优于PD控制，可用于工业设备的控制中。

Nonlinear friction compensation based on neural networks for the positioning table

A BP neural networks based compensation method for nonlinear friction in mechanical servo systems is presented. According to the proposed method the controller, which is composed of a traditional PD controller and an NN controller, is designed. It has both the merits of the PD controllers and the general approximation property of the neural networks, and can be employed to compensate the nonlinear friction and other disturbances. The stability of this method was proved using Lyapunov stability theory, and the boundary of the tracking error was derived as well. By using bristle dynamic friction model, simulations and experiments were conducted on an X Y positioning table. The experiment results demonstrate that the controller can compensate the nonlinear factors in the system, guarantee the system stability and diminish the tracking error. Therefore it can be applied in industries with better performance.