Please use this identifier to cite or link to this item:
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.
Issue Date: 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.
Type: article
DOI: 10.1002/rnc.5350
ISSN: 1049-8923
SCOPUS: 2-s2.0-85100114424
Appears in Collections:Faculty of Mechanical and Civil Engineering, Kraljevo

Page views(s)




Files in This Item:
File Description SizeFormat 
  Restricted Access
29.86 kBAdobe PDFThumbnail

Items in SCIDAR are protected by copyright, with all rights reserved, unless otherwise indicated.