Open AccessComparative analysis of Direct and Indirect Model Reference Adaptive Control by Extended Kalman Filter
Author(s) -
R. Vinothkanna,
M Duraipandian
Publication year - 2021
Publication title -
journal of electrical engineering and automation
Language(s) - English
Resource type - Journals
ISSN - 2582-3051
DOI - 10.36548/jeea.2021.3.001
Subject(s) - control theory (sociology) , nonlinear system , lyapunov function , computer science , reference model , kalman filter , equivalence (formal languages) , adaptive control , filter (signal processing) , control (management) , control engineering , mathematics , engineering , artificial intelligence , physics , software engineering , discrete mathematics , quantum mechanics , computer vision
Considerations about the increasing complexity of technological systems have stimulated the interest in hybrid systems that can successfully manage switching behaviour or approach nonlinearity. Hybrid systems are made up of two parts: a constant dynamics component and a switching mechanism. This article investigates the effectiveness of direct and indirect model adaptive control approaches for any common tool for hybrid modelling and approximation nonlinear systems. A reference model may be linear or partially refined, specifies the desired loop system behavior that the adaptive controllers are capable of achieving in the face of unknown system dynamics regardless of the system dynamics. Individual control gains are obtained for each subsystem and it is also carefully tuned to the altered behavior of each system. Through the application of dynamic gain adjustment, singularities in the principle of certainty equivalence are avoided indirectly. The state of the reference model is asymptotically monitored for both techniques by assuming that a shared Lyapunov feature is available for the switched reference model.