Fixed-time adaptive neural self-triggered decentralized control for stochastic nonlinear systems with strong interconnections

Abstract

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.