Quantized Control of Markov Jump Nonlinear Systems Based on Fuzzy Hidden Markov Model

This paper considers the problem of asynchronous guaranteed cost control (GCC) for nonlinear Markov jump systems with stochastic quantization. Hidden Markov model is used to describe the nonsynchronous controller and the random quantization phenomenon. Based on Takagi-Sugeno fuzzy technique and Lyap...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2019-07, Vol.49 (7), p.2420-2430
Main Authors: Dong, Shanling, Wu, Zheng-Guang, Shi, Peng, Su, Hongye, Huang, Tingwen
Format: Article
Language:English
Subjects:
Asynchronous controller
Control stability
Control systems
Controllers
Cybernetics
Feedback control
Fuzzy systems
hidden Markov model (HMM)
Hidden Markov models
Liapunov functions
Markov analysis
Markov chains
Markov processes
Measurement
Nonlinear control
Nonlinear systems
Quantization (signal)
random quantization
Symmetric matrices
Takagi–Sugeno (T–S) fuzzy technique
Upper bounds
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Description
Summary:This paper considers the problem of asynchronous guaranteed cost control (GCC) for nonlinear Markov jump systems with stochastic quantization. Hidden Markov model is used to describe the nonsynchronous controller and the random quantization phenomenon. Based on Takagi-Sugeno fuzzy technique and Lyapunov function approach, a sufficient condition is obtained, which can not only ensure the asymptotic stability of the closed-loop system and existence of the desired controller, but also can yield the minimal upper bound of GCC performance. Finally, two examples are provided to demonstrate the correctness and reliability of our developed approaches.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2018.2813279