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Observer‐based adaptive neural networks control for Markovian jump nonlinear systems with partial mode information and input saturation
Author(s) -
Cao Boqiang,
Nie Xiaobing
Publication year - 2021
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5642
Subject(s) - control theory (sociology) , nonlinear system , artificial neural network , backstepping , computer science , observer (physics) , controller (irrigation) , state observer , adaptive control , mode (computer interface) , sliding mode control , control (management) , artificial intelligence , physics , quantum mechanics , agronomy , biology , operating system
The observer‐based adaptive neural networks control problem is studied in this article for nonlinear Markovian jump systems (MJSs) with partial mode information and input saturation. The existing adaptive control scheme for MJSs works only when the Markov mode of system is completely known. For this reason, the concept of partial mode information of Markov chain is embedded into the framework of adaptive backstepping method and then a mode detector is set to emit the mode. To achieve the control objective, only the emitted mode is used to design the state observer and adaptive control scheme. Besides, an auxiliary signal is introduced in MJSs to compensate the effect of input saturation and this signal appears in controller as the input of neural networks instead of a separate term, which makes the form of proposed controller simpler than that in the literature. Finally, a numerical example and a practical example are provided to demonstrate the feasibility of our control scheme.