Robust Fixed-Time Model Reference Adaptive Control

TL;DR

This paper introduces a fixed-time convergence MRAC strategy for unknown LTI systems that relaxes excitation conditions, ensuring robust parameter estimation and tracking within a fixed time frame.

Contribution

It presents a novel parameter update law within indirect MRAC that guarantees fixed-time convergence under less restrictive excitation conditions.

Findings

The proposed method achieves fixed-time convergence of parameters and errors.

Simulation results validate the effectiveness and robustness of the approach.

The approach outperforms existing methods under practical excitation conditions.

Abstract

This article proposes a Model Reference Adaptive Control (MRAC) strategy to achieve fixed-time convergence of parameter estimation and tracking errors for unknown linear time-invariant systems, without relying on the persistence of excitation condition. Instead, it employs a less restrictive initial/interval excitation condition on the regressor matrix, enhancing practicality and ease of implementation in real-world scenarios. Our primary contribution is a novel parameter update law within the indirect MRAC framework, ensuring that parameter estimates converge within a fixed time, once the initial/interval excitation condition is met. This approach simplifies the practical requirements for adaptive control while guaranteeing robust performance against parameter uncertainty and external disturbances. Simulation results provide a comparison with the current literature to validate the effectiveness of this approach.