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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2014

01.05.2014 | Original Article

Stable iterative adaptive dynamic programming algorithm with approximation errors for discrete-time nonlinear systems

verfasst von: Qinglai Wei, Derong Liu

Erschienen in: Neural Computing and Applications | Ausgabe 6/2014

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Abstract

In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.
Vorheriger Artikel Distance approximation for two-phase test sample representation in face recognition
Nächster Artikel Application of intelligent sliding mode control with moving sliding surface for overhead cranes

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Metadaten
Titel
Stable iterative adaptive dynamic programming algorithm with approximation errors for discrete-time nonlinear systems
verfasst von
Qinglai Wei
Derong Liu
Publikationsdatum
01.05.2014
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-013-1361-7

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