Improved model free adaptive iterative learning control for linear motor sliding mode control
The two-dimensional linear motor is characterized by nonlinearity,uncertainty,multivariable and strong coupling,and its exact model is not available,meanwhile,the operational instability caused by parameter uptake and perturbation in the actual process makes its tracking control very difficult. To address the above problems,based on the characteristics of model free adaptive iterative learning control that does not depend on the accurate mathematical model of the controlled system and the sliding mode control that can be designed independently of the object parameters and perturbations,a composite con-trol strategy was proposed to improve the model free adaptive iterative learning sliding mode structure. Then,the exponential convergence law sliding mode control was designed on the basis of the tight-form dynamic linearization,so that the improved model free adaptive iterative learning sliding mode control composite strategy can overcome the instability phenomenon and has strong robustness,thus further im-proving the system response speed and control accuracy. Finally,the control accuracy was stabilized within 1 μm by simulation and physical verification,and the accuracy and effectiveness of the proposed method was verified compared with other control schemes.
improved model free adaptive iterative learning controlsliding mode controltwo-dimensional linear motorerror rate of changecriterion function
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