Dynamic event-triggered control for discrete-time nonlinear Markov jump systems using policy iteration-based adaptive dynamic programming

DOI10.1016/j.nahs.2023.101338zbMath1520.93324OpenAlexW4322102674MaRDI QIDQ6171325

Khalid H. Alharbi, Huanqing Wang, Xiao-Heng Chang, Liang Zhang, Fanghua Tang

Publication date: 18 July 2023

Published in: Nonlinear Analysis. Hybrid Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.nahs.2023.101338

zbMATH Keywords

neural networkpolicy iterationadaptive dynamic programmingMarkov jump systemsdynamic event-triggered mechanism

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Adaptive control/observation systems (93C40) Discrete-time control/observation systems (93C55) Dynamic programming (90C39) Discrete event control/observation systems (93C65) Stochastic systems in control theory (general) (93E03)

Related Items (9)

Event-triggered fault-tolerant control for input-constrained nonlinear systems with mismatched disturbances via adaptive dynamic programming ⋮ Adaptive optimized consensus control for a class of nonlinear multi-agent systems with asymmetric input saturation constraints and hybrid faults ⋮ Finite-time asynchronous \(H_\infty\) control for Markov jump singularly perturbed systems with partially known probabilities ⋮ Distributed consensus of heterogeneous switched nonlinear multiagent systems with input quantization and DoS attacks ⋮ Global event-triggered output-feedback stabilization for switched nonlinear systems with time-delay and measurement sensitivity ⋮ Supervisory-based quantised hands-off control for a class of uncertain systems with nonlinear sector ⋮ Event-trigger-based composite adaptive fuzzy control for nonlinear time-varying state constraint systems with asymmetric input saturation ⋮ Active disturbance rejection‐based event‐triggered bipartite consensus control for nonaffine nonlinear multiagent systems ⋮ Neural network-based adaptive optimal containment control for non-affine nonlinear multi-agent systems within an identifier-actor-critic framework

Cites Work

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