Multi-Parametric Extremum Seeking-based Auto-Tuning for Robust Input-Output Linearization Control

TL;DR

This paper introduces a novel approach combining robust input-output linearization with multi-parametric extremum seeking to automatically tune feedback gains in nonlinear systems, enhancing robustness and performance.

Contribution

It presents a new integrated control scheme that adaptively tunes feedback gains for nonlinear systems using model-free extremum seeking, ensuring stability and improved tracking.

Findings

Demonstrates effective auto-tuning on a mechatronics example

Ensures stability of combined robust control and extremum seeking

Improves output tracking performance in uncertain nonlinear systems

Abstract

We study in this paper the problem of iterative feedback gains tuning for a class of nonlinear systems. We consider Input-Output linearizable nonlinear systems with additive uncertainties. We first design a nominal Input-Output linearization-based controller that ensures global uniform boundedness of the output tracking error dynamics. Then, we complement the robust controller with a model-free multi-parametric extremum seeking (MES) control to iteratively auto-tune the feedback gains. We analyze the stability of the whole controller, i.e. robust nonlinear controller plus model-free learning algorithm. We use numerical tests to demonstrate the performance of this method on a mechatronics example.