A Fast Finite-Time Neural Network Control of Stochastic Nonlinear Systems

Fang Wang, Zhaoyang You, Zhi Liu, C. L. Philip Chen

2022IEEE Transactions on Neural Networks and Learning Systems58 citationsDOI

Abstract

This article takes a fast finite-time control of stochastic nonlinear systems into account. The presence of unknown stochastic disturbance terms makes the traditional fast finite-time control approaches unavailable. To deal with this difficulty, by establishing an auxiliary function and using Jensen's inequality, in Lemma 6, a new criterion of fast finite-time stability is first established for the uncertain stochastic system. Based on the approximation ability of neural networks (NNs), an innovative fast finite-time strategy is put forward for stochastic nonlinear systems. Furthermore, by adopting the presented fast finite-time stability criterion, the stability of the stochastic systems is confirmed. Finally, two simulations are implemented to validate the feasibility of the presented NN control strategy.

Topics & Concepts

Nonlinear systemArtificial neural networkLemma (botany)Stability (learning theory)Control theory (sociology)Stochastic neural networkComputer scienceStochastic controlMathematical optimizationControl (management)MathematicsRecurrent neural networkOptimal controlArtificial intelligenceMachine learningPoaceaeBiologyEcologyPhysicsQuantum mechanicsAdaptive Control of Nonlinear SystemsAdaptive Dynamic Programming ControlNeural Networks and Applications