Regression Filtration with Resetting to Provide Exponential Convergence of MRAC for Plants with Jump Change of Unknown Parameters

TL;DR

This paper introduces a novel adaptive control method that achieves exponential convergence without persistent excitation, using a resetting filtration scheme to handle jump changes in plant parameters, improving transient response.

Contribution

It proposes a new adaptive law with resetting filtration that relaxes PE to FE, ensuring exponential convergence and better transient response during parameter jumps.

Findings

The method guarantees exponential convergence under finite excitation.

Numerical experiments show improved transient response.

The approach outperforms traditional composite adaptive laws.

Abstract

This paper proposes a new method to provide the exponential convergence of both the parameter and tracking errors of the composite adaptive control system without the persistent excitation (PE) requirement. Instead, the derived composite adaptive law ensures the above-mentioned properties under the strictly weaker finite excitation (FE) condition. Unlike known solutions, in addition to the PE requirement relaxation, it provides better transient response under jump change of the plant uncertainty parameters. To derive such an adaptive law, a novel scheme of uncertainty filtration with resetting is proposed, which provides the required properties of the control system. A rigorous proof of all mentioned properties of the developed adaptive law is presented. Such law is compared with the known composite ones, which also relax the PE requirement, using the wing-rock problem to conduct numerical experiments. The obtained results fully support the theoretical analysis and demonstrate the advantages of the proposed method.