Dynamic event-triggered tolerant containment control protocol for discrete multiagent systems based on finite index policy iteration algorithm

For tolerant containment control of multi-agent systems, considering the challenges in modeling and the impact of actuator faults on system security and reliability, a finite index dynamic event-triggered policy iteration algorithm is proposed. This algorithm only requires input and output data, wit...

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Bibliographic Details
Published in:ISA transactions Vol. 158; pp. 21 - 31
Main Authors: Yan, Shuya, Li, Xiaocong, Qian, Huaming, Al Mamun, Abdullah
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.03.2025
Subjects:
Dynamic event-triggered mechanism
Policy iteration algorithm
Reinforcement learning
Time-varying actuator faults
Tolerant containment control
Time-varying actuator faults
Policy iteration algorithm
Tolerant containment control
Dynamic event-triggered mechanism
Reinforcement learning
ISSN:0019-0578, 1879-2022, 1879-2022
Online Access:Get full text
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Summary:For tolerant containment control of multi-agent systems, considering the challenges in modeling and the impact of actuator faults on system security and reliability, a finite index dynamic event-triggered policy iteration algorithm is proposed. This algorithm only requires input and output data, without relying on system models, and simultaneously considers the faults and energy consumption issues to improve the system reliability and save energy consumption. The conditions are provided to demonstrate the convergence and optimality of the algorithm, including a convergence speed, that is, the number of iterations required for convergence is finite. For the convenience of practical implementation, an actor–critic structure is adopted and an actor network weight tuning law with actuator fault factors is designed to more accurately approximate the control protocol in policy iteration algorithm. In addition, an event-triggered mechanism is employed in saving computational resources. Finally, simulation results verify the efficiency of the designed algorithm. •Simultaneously consider the system’s tolerance and energy consumption issues.•The convergence and optimality proofs for the algorithm are provided.•The actor network weight is adjusted according to the actuator fault factors.•The dynamic event-triggered mechanism saves computing resources.
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ISSN:0019-0578
1879-2022
1879-2022
DOI:10.1016/j.isatra.2024.12.044