Machine Tool Servo System Tracking Based on Nonparametric Model Identification and Optimal Iterative Learning Control
Based on the time-varying characteristics of machine tool servo systems,a NMI-OILC algorithm is proposed,which deeply integrates optimal iterative learning control(OILC)and non-parametric model(NMI)recognition.Machine servo systems are continuously identified to effectively address time-varying defects that OILC is unable to cope with.The algorithm is used to control the servo system motion process and has strong tracking performance.The simulation results show that the performance of NMI-OILC is temporarily degraded under slow parameter changes,but gradually converges after several iterations,and the tracking error is also satisfactory.The NMI-OILC provides excellent tracking performance even when the parameters change.Experimental results show that the NMI-OILC algorithm can gradually converge the objective function after the system parameters change,and the tracking error meets the control requirements.It can effectively cope with the time-varying characteristics of the system,and significantly improve the tracking capability of the system.This research can be extended to other fields of parameter identification of mechanical transmission and has good application value.
iterative learningservo systemparameter identificationoptimal control
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