Composite Learning Adaptive Control without Excitation Condition

Abstract

This paper focuses on excitation collection and composite learning adaptive control design for uncertain nonlinear systems. By adopting the spectral decomposition technique, a linear regression equation is constructed to collect previously appeared excitation information, establishing a relationship between unknown parameters and the system's historical data. A composite learning term, developed using the linear regression equation, is incorporating into the Lyapunov-based parameter update law. In comparison to the existing results, all spectrums of previously appeared excitation information are collected, with the matrices in linear regression equation guaranteed to be bounded. This paper introduces concepts of excited and unexcited subspaces for analyzing the parameter estimation errors, and a novel Lyapunov function is developed for stability analysis. It is demonstrated that, without imposing any excitation condition, the state and excited parameter estimation error component converge to zero, while the unexcited component remains unchanged. Simulation results are provided to validate the theoretical findings.

Tasks

Reproductions