基于改进灰狼算法的非线性摩擦力辨识与补偿方法研究

Research on Nonlinear Friction Identification and Compensation Method Based on Improved Grey Wolf Algorithm

为减小非线性摩擦力对机床伺服系统运动控制精度的影响,对机床伺服系统摩擦特性进行研究,将改进灰狼算法应用于LuGre摩擦模型参数辨识,通过搭建伺服系统动力学模型进行摩擦前馈补偿仿真,并在三轴数控雕铣机试验平台进一步完成摩擦前馈补偿验证。结果表明:应用基于改进灰狼算法辨识的LuGre摩擦模型前馈补偿方法,在两轴联动圆轨迹运动跟随过程中,低速运动换向时最大跟随误差下降幅度为67.7%,高速运动条件下误差下降幅度为84.0%,有效消除了非线性摩擦在速度换向过程中产生的负面影响,证明该方法在非线性摩擦力辨识方面具有优异的效果,有助于提高机床伺服系统的运动控制精度。

In order to reduce the influence of nonlinear friction on the motion control accuracy of the machine tool servo system,the friction characteristics of the machine tool servo system were investigated,the improved grey wolf algorithm was applied to the parameter identification of the LuGre friction model,and the friction feed-forward compensation simulation was carried out through the construction of the servo system dynamics model,and the friction feed-forward compensation was further verified on the experimental platform of the three-axis CNC engraving and milling machine.The experimental results show that using the feedforward compensation method of LuGre friction model based on improved grey wolf algorithm identification,in the process of two-axis linkage circular trajectory motion following,the maximum following error decreases by 67.7%in low speed motion reversing,and by 84.0%in high speed motion.The negative effect of nonlinear friction in the process of speed reversal is effectively eliminated,which proves that the method has excellent effect in the identification of nonlinear friction,and helps to improve the motion control accuracy of machine tool servo system.