Wavelet Based Iterative Learning Control with Fuzzy PD Feedback for Position Tracking of A Pneumatic Servo System

TL;DR

This paper introduces a wavelet-based iterative learning control combined with fuzzy PD feedback to enhance position tracking in pneumatic systems with nonlinearities and uncertainties, demonstrating improved performance through simulations.

Contribution

It presents a novel wavelet-based iterative learning control scheme integrated with fuzzy PD feedback and genetic algorithm optimization for rule inference.

Findings

Significantly improved position tracking accuracy.

Effective attenuation of unlearnable dynamics.

Enhanced control performance in pneumatic systems.

Abstract

In this paper, a wavelet-based iterative learning control (WILC) scheme with Fuzzy PD feedback is presented for a pneumatic control system with nonsmooth nonlinearities and uncertain parameters. The wavelet transform is employed to extract the learnable dynamics from measured output signal before it can be used to update the control profile. The wavelet transform is adopted to decompose the original signal into many low-resolution signals that contain the learnable and unlearnable parts. The desired control profile is then compared with the learnable part of the transformed signal. Thus, the effects from unlearnable dynamics on the controlled system can be attenuated by a Fuzzy PD feedback controller. As for the rules of Fuzzy PD controller in the feedback loop, a genetic algorithm (GA) is employed to search for the inference rules of optimization. A proportional-valve controlled pneumatic cylinder actuator system is used as the control target for simulation. Simulation results have shown a much-improved positiontracking performance.