[en] Composite adaptive control (CAC) that integrates direct and indirect adaptive control techniques can achieve smaller tracking errors and faster parameter convergence compared with direct and indirect adaptive control techniques. However, the condition of persistent excitation (PE) still has to be satisfied to guarantee parameter convergence in CAC. This paper proposes a novel model reference composite learning control (MRCLC) strategy for a class of affine nonlinear systems with parametric uncertainties to guarantee parameter convergence without the PE condition. In the composite learning, an integral during a movingtime window is utilized to construct a prediction error, a linear filter is applied to alleviate the derivation of plant states, and both the tracking error and the prediction error are applied to update parametric estimates. It is proven that the closed-loop system achieves global exponential-like stability under interval excitation rather than PE of regression functions. The effectiveness of the proposed MRCLC strategy has been verified by the application to an inverted pendulum control problem.
Centre de recherche :
SnT
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
PAN, Lin ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Composite Learning Control With Application to Inverted Pendulums
Date de publication/diffusion :
01 décembre 2015
Nom de la manifestation :
Chinese Automation Congress (CAC), 2015
Organisateur de la manifestation :
Chinese Automation Congress (CAC), 2015
Lieu de la manifestation :
Wuhan, Chine
Date de la manifestation :
from 26-11-2015 to 30-11-2015
Sur invitation :
Oui
Manifestation à portée :
International
Titre de l'ouvrage principal :
IEEE International Conference - Chinese Automation Congress (CAC), 2015
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