Study on Iterative Learning Control bandwidth tuning using particle swarm optimization technique for high precision motion

This paper utilized the improved particle swarm optimization (IPSO) technique for adjusting the gains of PID controller, Iterative Learning Control (ILC) and the bandwidth of zero-phase Butterworth filter of the ILC. The conventional ILC learning process has the potential to excite rich frequency contents and try to learn the error signals. However the learnable and unlearnable error signals should be separated for bettering control process as repetition goes. Producing high frequency error condition should be avoided before the phase margin caused any trouble. The filter bandwidth should be changed at every repetition. Thus adaptive bandwidth in the ILC controller with the aid of IPSO tuning is proposed here. Simulation results show the new controller can cancel the errors efficiently as repetition goes. The correlation coefficient validates the learnable compensated error signal for the trajectory is adaptively decomposed from previous error history via the bandwidth tuning mechanism in next repetition. The learnable error signals of the Intrinsic Mode Functions (IMFs) through the Empirical Mode Decomposition (EMD) correlate efficiently with reduced tracking error as repetition goes. Simulation results validate the application for positioning of a robot arm system for high precision motion control.