Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/12607
Title: Adaptive optimization algorithm for nonlinear Markov jump systems with partial unknown dynamics
Authors: Fang H.
Zhu G.
Stojanović, Vladimir
Nie R.
He J.
Luan X.
LIU F.
Journal: International Journal of Robust and Nonlinear Control
Issue Date: 1-Jan-2021
Abstract: © 2021 John Wiley & Sons, Ltd. An online adaptive optimal control problem for a class of nonlinear Markov jump systems (MJSs) is studied. It is worth noting that the dynamic information of MJSs is partially unknown. Applying the neural network linear differential inclusion techniques, the nonlinear terms in MJSs are approximately converted to linear forms. By using subsystem transformation schemes, we can transfer the nonlinear MJSs to N new coupled linear subsystems. Then a new online policy iteration algorithm is put forward to obtain the adaptive optimal controller. Some theorems are given afterward to ensure the convergence of the new algorithm. At last, a simulation example is provided to verify the applicability of the algorithm.
URI: https://scidar.kg.ac.rs/handle/123456789/12607
Type: Article
DOI: 10.1002/rnc.5350
ISSN: 10498923
SCOPUS: 85100114424
Appears in Collections:Faculty of Mechanical and Civil Engineering, Kraljevo
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