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An asymptotic decoupling approach for adaptive control with unmeasurable coupled dynamics
Author(s) -
Dogan K. Merve,
Yucelen Tansel,
Muse Jonathan A.
Publication year - 2021
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3191
Subject(s) - decoupling (probability) , control theory (sociology) , generalization , convergence (economics) , stability (learning theory) , adaptive control , dynamical systems theory , system dynamics , computer science , exponential stability , linear matrix inequality , mathematics , control (management) , mathematical optimization , control engineering , nonlinear system , engineering , artificial intelligence , physics , mathematical analysis , quantum mechanics , machine learning , economics , economic growth
Summary While adaptive control methods have the capability to suppress the effect of system uncertainties without excessive reliance on dynamical system models, their stability can be adversely affected in the presence of coupled dynamics. Motivated by this standpoint, the contribution of this article is a decoupling approach for model reference adaptive control algorithms. The key feature of the proposed framework is that it guarantees asymptotic convergence between the trajectories of an uncertain dynamical system and a given reference model without relying on any measurements from the coupled dynamics under a tight sufficient stability condition. We also provide a generalization to address the uncertainty in the control effectiveness matrix, where the resulting sufficient stability condition in this case relies on linear matrix inequalities. Finally, numerical examples are provided to illustrate the efficacy of the presented theoretical results.