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Fixed-time Adaptive Neural Self-triggered Decentralized Control for Stochastic Nonlinear Systems with Strong Interconnections by Zhechen Zhu; Quanxin Zhu is a Computer Science article available to read on EtoBox.

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This article develops a fixed-time adaptive self-triggered decentralized control strategy based on the neural network for a class of stochastic nonlinear systems with strong interconnections. With the help of the special property of the Gaussian function, the strong interconnected functions in stochastic systems can be handled successfully without matching conditions assumptions, and the processing conditions of strong interconnected functions are relaxed. Moreover, a self-triggered mechanism is designed to decrease the waste of the system communication resources. On this basis, a fixed-time adaptive self-triggered decentralized control scheme is constructed by utilizing the fixed-time stability theory such that the stochastic nonlinear system with strong interconnections is fixed-time stable in probability. Finally, the feasibility of the developed control strategy can be ensured by the simulation result.

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Author

Zhechen Zhu; Quanxin Zhu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)