Iterative learning control bandwidth tuning using the particle swarm optimization technique for high precision motion

Abstract

This paper utilizes the improved particle swarm optimization (IPSO) technique for adjusting the gains of PID controller, iterative learning control (ILC) and the bandwidth of Butterworth filter of the ILC. The conventional ILC learning process has the potential to excite rich frequency contents and to learn the error signals. However the learnable and unlearnable error signals should be separated for b ettering control process along with the repetitions. Since producing a high frequency error condition should be avoided before the phase margin cause any trouble. Learnable error signals through a bandwidth tuning mechanism should be adaptively injected into learning control laws and thus reduce the tracking error effectively at every repetition. The filter bandwidth should be changed at every repetition for the shape of compensated errors at frequency response thinking. 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 the process is repeated. The correlation coefficient that validates the learnable compensated error signal for the trajectory is adaptively decomposed from previous error history via the bandwidth tuning mechanism in the next repetition. The learnable error signals of the intrinsic mode functions through the empirical mode decomposition correlate efficiently with reduced tracking error with further repetitions. Simulation results validate the effectiveness of the IPSO-ILC for precision motion control of a robot arm link performing high speed maneuvering or computer numerical control commanding axis positioning.