Open AccessModel reference composite learning control without persistency of excitation
Author(s) -
Pan Yongping,
Zhang Jun,
Yu Haoyong
Publication year - 2016
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2016.0032
Subject(s) - control theory (sociology) , tracking error , convergence (economics) , parametric statistics , exponential smoothing , computer science , exponential stability , stability (learning theory) , adaptive control , mathematical optimization , mathematics , control (management) , artificial intelligence , nonlinear system , machine learning , statistics , physics , quantum mechanics , economics , computer vision , economic growth
Parameter convergence is desirable in adaptive control as it brings several attractive features, including accurate online modelling, exponential tracking, and robust adaptation without parameter drift. However, a strong persistent‐excitation (PE) condition must be satisfied to guarantee parameter convergence in the conventional adaptive control. This study proposes a model reference composite learning control strategy to guarantee parameter convergence without the PE condition. In the composite learning, an integral at a moving‐time window is applied to construct a prediction error, an integral transformation is derived for avoiding the time derivation of plant states in the calculation of the prediction error, and both the tracking error and the prediction error are applied to update parametric estimates. Global exponential stability of the closed‐loop system is established under an interval‐excitation condition which is much weaker than the PE condition. Compared with a concurrent learning technique that has the same aim as this study, the proposed composite learning technique avoids the usage of singular value maximisation and fixed‐point smoothing resulting in a considerable reduction of computational cost. Numerical results have verified effectiveness and superiority of the proposed control strategy.