Asynchronous robust dynamic output feedback H∞ control for fuzzy stochastic hybrid systems subject to time-varying delays and hidden Markov model

• Published in: Soft computing (Berlin, Germany) Vol. 27; no. 1; pp. 201 - 218
• Main Authors: Lin, Yuqian, Zhuang, Guangming, Xia, Jianwei, Sun, Wei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2023
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Engineering; Fuzzy Systems and Their Mathematics; Mathematical Logic and Foundations; Mechatronics; Robotics; Dynamic output feedback; Hidden Markov model; Asynchronous control; Neural networks; Takagi–Sugeno fuzzy systems
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-022-07575-x

In this paper, we studied the event-triggered dynamic output feedback asynchronous robust H ∞ control for Takagi–Sugeno fuzzy Markovian jump neural networks with mode-dependent time-varying delays. The main aim is to design fuzzy dynamic output feedback controller under asynchrony and event-triggered mechanisms to ensure the stochastic stability and H ∞ performance of closed-loop fuzzy Markovian jump neural networks with time-varying delays. Hidden Markov model strategy is used to describe asynchronous scheme; an event-triggered mechanism is utilized to reduce the consumption of communication resources. An improved mode-dependent and delay dependent L–K functional is constructed to address robust stochastic stability, and parallel distribution compensation technology is exploited to realize the fuzzy dynamic output feedback controller in terms of linear matrix inequalities. Finally, numerical examples including a synthetic transcriptional regulatory oscillatory network are provided to demonstrate the method’s efficiency.