Sampled-data-based dynamic event-triggered asynchronous control of continuous-time positive Markov jump systems

Kai Yin and Dedong Yang

Chaos, Solitons & Fractals, 2023, vol. 169, issue C

Abstract: The L1-gain asynchronous control issue of continuous-time positive Markov jump systems has been investigated using an event-triggered method. First, in light of positivity and Zeno behavior, a linear programming dynamic event-triggered method based on sampled data is introduced to regulate the data sending from system feedback state to the controller, which can eliminate Zeno behavior and reduce the network resources occupied by frequent data sending. Since the controller does not receive the system mode information accurately, the asynchronous phenomenon is taken into account via a hidden Markov model. Through the introduction of auxiliary variables and the development of a novel co-positive Lyapunov functional, the positivity of the closed-loop positive Markov jump systems is examined, and the stability condition is achieved. Then, a cooperative design scheme between a desired sampled-data-based dynamic event-triggered L1-gain asynchronous controller and the sampled-data-based dynamic event-triggered method is formulated. Finally, a numerical example shows the effectiveness of this work in this paper.

Keywords: Hidden Markov model; Linear programming; Sampled-data-based dynamic event-triggered method; Co-positive Lyapunov functional (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:169:y:2023:i:c:s0960077923001558

DOI: 10.1016/j.chaos.2023.113254

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