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Neural Computing and Applications

Erschienen in:

01.02.2013 | ISNN 2011

A neural-network-based iterative GDHP approach for solving a class of nonlinear optimal control problems with control constraints

verfasst von: Ding Wang, Derong Liu, Dongbin Zhao, Yuzhu Huang, Dehua Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 2/2013

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Abstract

In this paper, a novel neural-network-based iterative adaptive dynamic programming (ADP) algorithm is proposed. It aims at solving the optimal control problem of a class of nonlinear discrete-time systems with control constraints. By introducing a generalized nonquadratic functional, the iterative ADP algorithm through globalized dual heuristic programming technique is developed to design optimal controller with convergence analysis. Three neural networks are constructed as parametric structures to facilitate the implementation of the iterative algorithm. They are used for approximating at each iteration the cost function, the optimal control law, and the controlled nonlinear discrete-time system, respectively. A simulation example is also provided to verify the effectiveness of the control scheme in solving the constrained optimal control problem.

Vorheriger Artikel Global stability analysis of multitime-scale neural networks

Nächster Artikel The finite-horizon optimal control for a class of time-delay affine nonlinear system

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Titel: A neural-network-based iterative GDHP approach for solving a class of nonlinear optimal control problems with control constraints
verfasst von: Ding Wang
Derong Liu
Dongbin Zhao
Yuzhu Huang
Dehua Zhang
Publikationsdatum: 01.02.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 2/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0707-2

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