基于PSO算法的直接进给轴摩擦特性研究

Reasearch on Friction Behavior for Direct Feed Drive Based on Intelligent Algorithm

直线电机驱动的进给轴在进给过程中易受到导轨滑动摩擦特性影响而存在高度的非线性特性,非线性摩擦是影响直线电机驱动的伺服系统动静态性能的主要因素。提出了一种新型的直接进给轴非线性摩擦迟滞模型,并给出该摩擦模型的参数辨识策略,建立了直接进给轴摩擦非线性迟滞特性的综合数学模型,并在建立的数学模型的基础上建立了基于Simulink的仿真模型。以基于电流反馈检测为手段,采集了位移和所对应迟滞摩擦力的样本点。以采集的样本点为基础,给出了通过粒子群算法(PSO)辨识摩擦迟滞模型的方法。最后,通过Simulink仿真平台验证了所提出的的摩擦迟滞模型和模型参数辨识方法的准确性。仿真结果表明,辨识的摩擦模型能准确的反映直接进给轴的摩擦分布特性,提出的直接进给轴迟滞摩擦模型参数辨识方法具有可行性。

Direct feed drive is susceptible to sliding friction behavior of the rail in the feeding movement,which has seriousnonlinear behavior.Nonlinear friction is the main factor that influences the dynamic and static performance of the servo systemdriven.In this paper,a new nonlinear friction hysteresis model for direct feed shaft is presented.The parameter identificationmethod of friction model is proposed,and the mathematical model of the direct feed shaft considering the nonlinear hysteresischaracteristics is established.Based on the mathematical model,the simulation model through Simulink is established.Throughthe current feedback detection method,the displacement and the corresponding hysteresis friction force are collected.Based onthe collected sample points,the method of identifying the friction hysteresis model by using particle swarm optimization(PSO)ispresented.Finally,the accuracy of the proposed model is verified by Simulink simulation platform.The simulation results show thatthe identification of the friction model can accurately reflect the characteristics of the direct feed shaft friction distribution.