Multiple-Model Adaptive Control With Set-Valued Observers

TL;DR

This paper introduces a multiple-model adaptive control approach using set-valued observers to robustly identify plant parameters and ensure stability despite significant uncertainties, demonstrated through simulation.

Contribution

It presents a novel MMAC-SVO methodology combining set-valued observers with adaptive control for uncertain plants, addressing computational challenges.

Findings

Provides robust stability guarantees for uncertain plants

Uses set-valued observers to identify parameter regions

Demonstrates effectiveness through simulation results

Abstract

This paper proposes a multiple-model adaptive control methodology, using set-valued observers (MMAC-SVO) for the identification subsystem, that is able to provide robust stability and performance guarantees for the closed-loop, when the plant, which can be open-loop stable or unstable, has significant parametric uncertainty. We illustrate, with an example, how set-valued observers (SVOs) can be used to select regions of uncertainty for the parameters of the plant. We also discuss some of the most problematic computational shortcomings and numerical issues that arise from the use of this kind of robust estimation methods. The behavior of the proposed control algorithm is demonstrated in simulation.