直线电机伺服系统的自适应模糊摩擦补偿

Adaptive fuzzy friction compensation for linear motor servo systems

针对受摩擦力影响,接触运动的直线电机在低速运动时速度常会不平稳、系统控制精度降低的问题,提出一种自适应摩擦补偿方法。通过模糊模型参数的自适应调整,实现摩擦力值的在线估计,并据此进行摩擦补偿,以克服摩擦对电机性能的影响。该方法采用复合自适应律,同时利用系统输出误差与参数估计误差的相关信息进行参数调整,以提高模糊模型参数收敛的速度,使参数估计值在一定条件下收敛到最优值。并在理论上证明了该方法的闭环稳定性与参数收敛性。仿真结果表明,此方法能实现摩擦的在线估计与补偿,从而提高直线电机的控制性能。

Due to the influence of friction, the linear motor with contact surface frequently appears rough running and control precisiou deterioration during low speed operation. To overcome this problem, an adaptive friction compensation method was proposed. Through tuning the parameters of a fuzzy model adaptively, the fi＇iction model was estimated. Then the friction was compensated according to its estimate, consequently the influence of friction could be eliminated. This method used composite adaptation, which utilized the relative information of output tracking error and parameter estimation error simultaneously, to accelerate the parameter convergence. By using composite adaptation, the parameters of the fuzzy model could converge to the optimal values under certain conditions. In this paper, the close loop stability and parameter estimate convergence for the proposed method were proved. Simulation demonstrates that the proposed method can realize good online friction estimation and compensation, hence the control performance of the linear motor is improved.