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Approximate Dynamic Programming for Self-Learning Control

Derong Liu

Derong Liu. Approximate Dynamic Programming for Self-Learning Control. 自动化学报, 2005, 31(1): 13-18.
引用本文: Derong Liu. Approximate Dynamic Programming for Self-Learning Control. 自动化学报, 2005, 31(1): 13-18.
Derong Liu. Approximate Dynamic Programming for Self-Learning Control. ACTA AUTOMATICA SINICA, 2005, 31(1): 13-18.
Citation: Derong Liu. Approximate Dynamic Programming for Self-Learning Control. ACTA AUTOMATICA SINICA, 2005, 31(1): 13-18.

Approximate Dynamic Programming for Self-Learning Control

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    通讯作者:

    Derong Liu

Approximate Dynamic Programming for Self-Learning Control

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    Corresponding author: Derong Liu
  • 摘要: This paper introduces a self-learning control approach based on approximate dynamic programming. Dynamic programming was introduced by Bellman in the 1950’s for solving optimal control problems of nonlinear dynamical systems. Due to its high computational complexity, the applications of dynamic programming have been limited to simple and small problems. The key step in finding approximate solutions to dynamic programming is to estimate the performance index in dynamic programming. The optimal control signal can then be determined by minimizing (or maximizing) the performance index. Artificial neural networks are very efficient tools in representing the performance index in dynamic programming. This paper assumes the use of neural networks for estimating the performance index in dynamic programming and for generating optimal control signals, thus to achieve optimal control through self-learning.
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出版历程
  • 收稿日期:  2004-02-18
  • 修回日期:  2004-07-27
  • 刊出日期:  2005-01-20

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