Improving Initial Transients of Online Learning Echo State Network Control System with Feedback Adjustments

TL;DR

This paper introduces a feedback P-D controller to improve the initial convergence speed of online learning echo state network control systems, enhancing robustness and ease of deployment.

Contribution

It presents a simple feedback adjustment method that significantly accelerates initial convergence of online ESN controllers without prior knowledge or complex modifications.

Findings

Rapid initial convergence demonstrated in simulations

Enhanced robustness against system changes

Easy to implement and deploy in practical systems

Abstract

Echo state networks (ESNs) have become increasingly popular in online learning control systems due to their ease of training. However, online learning ESN controllers often suffer from slow convergence during the initial transient phase. Existing solutions, such as prior training, control mode switching, and incorporating plant dynamic approximations, have notable drawbacks, including undermining the system's online learning property or relying on prior knowledge of the controlled system. This work proposes a simple yet effective approach to address the slow initial convergence of online learning ESN control systems by integrating a feedback proportional-derivative (P-D) controller. Simulation results demonstrate that the proposed control system achieves rapid convergence during the initial transient phase and shows strong robustness against changes in the controlled system's dynamics and variations in the online learning model's hyperparameters. We show that the feedback controller accelerates convergence by guiding the online learning ESN to operate within a data range well-suited for learning. This study offers practical benefits for engineers aiming to implement online learning ESN control systems with fast convergence and easy deployment.