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

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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.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grants 60874043, 60904037, 60921061, and 61034002, by Beijing Natural Science Foundation under Grant 4102061, and by the National Science Foundation of USA under Grant ECCS-1027602.

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Authors and Affiliations

  1. Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Ding Wang, Derong Liu, Dongbin Zhao, Yuzhu Huang & Dehua Zhang

  2. Department of Electrical and Computer Engineering, University of Illinois, Chicago, IL, 60607, USA

    Derong Liu

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  1. Ding Wang
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  2. Derong Liu
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  3. Dongbin Zhao
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  4. Yuzhu Huang
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  5. Dehua Zhang
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Correspondence to Ding Wang.

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Wang, D., Liu, D., Zhao, D. et al. A neural-network-based iterative GDHP approach for solving a class of nonlinear optimal control problems with control constraints. Neural Comput & Applic 22, 219–227 (2013). https://doi.org/10.1007/s00521-011-0707-2

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  • DOI: https://doi.org/10.1007/s00521-011-0707-2

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