Lyapunov Analysis of Least Squares Based Direct Adaptive Control

TL;DR

This paper develops a Lyapunov-based analysis framework to integrate recursive least squares parameter identification with direct adaptive control, demonstrating improved convergence and robustness in adaptive cruise control simulations.

Contribution

It introduces a novel Lyapunov-like function enabling direct adaptive control with LS-based parameter estimation, which was previously challenging.

Findings

Achieves exponential convergence of parameters

Validates approach through Matlab/Simulink and CarSim simulations

Demonstrates improved transient performance over gradient methods

Abstract

Adaptive control strategies usually are designed based on gradient methods for the sake of simplicity in Lyapunov analysis. However, least squares (LS)-based parameter identifiers, with proper selection of design parameters, exhibit better transient performance than the gradient-based ones, from the aspects of convergence speed and robustness to measurement noise. On the other hand, most of the LS-based adaptive control procedures are designed via the indirect adaptive control approaches, due to the difficulty in integrating an LS-based adaptive law within the direct approaches starting with a certain Lyapunov-like cost function to be driven to (a neighborhood of) zero. In this paper, a formal constructive analysis framework is proposed to integrate the recursive LS-based parameter identification with direct adaptive control. To this end, a Lyapunov-like function is proposed for the analysis to achieve adaptive laws, which guarantee the exponential convergence of the parameters. Application of the proposed procedure in adaptive cruise control design is studied through Matlab/Simulink and CarSim simulations, validating the analytical results.