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Soft Computing
Erschienen in: Soft Computing 3/2020

04.05.2019 | Methodologies and Application

Stabilization of a class of nonlinear control systems via a neural network scheme with convergence analysis

verfasst von: Alireza Nazemi, Marziyeh Mortezaee

Erschienen in: Soft Computing | Ausgabe 3/2020

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Abstract

In this paper, the stability of a class of nonlinear control systems is analyzed. We first construct an optimal control problem by inserting a suitable performance index; this problem is referred to as an infinite horizon problem. By a suitable change of variable, the infinite horizon problem is reduced to a finite horizon problem. We then present a feedback controller designing approach for the obtained finite horizon control problem. This approach involves a neural network scheme for solving the nonlinear Hamilton Jacobi Bellman equation. By using the neural network method, an analytic approximate solution for value function and a suboptimal feedback control law are achieved. A learning algorithm based on a dynamic optimization scheme with stability and convergence properties is also provided. Some illustrative examples are employed to demonstrate the accuracy and efficiency of the proposed plan. As a real-life application in engineering, the stabilization of a micro-electromechanical system is studied.
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Metadaten
Titel
Stabilization of a class of nonlinear control systems via a neural network scheme with convergence analysis
verfasst von
Alireza Nazemi
Marziyeh Mortezaee
Publikationsdatum
04.05.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 3/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-019-04024-0

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