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作者:

Gao, Ning (Gao, Ning.) | Wang, Ding (Wang, Ding.) (学者:王鼎) | Zhao, Mingming (Zhao, Mingming.) | Hu, Lingzhi (Hu, Lingzhi.)

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摘要:

The core of the optimal tracking control problem for nonlinear systems is how to ensure that the controlled system tracks the desired trajectory. The utility functions in previous studies have different properties which affect the final tracking effect of the intelligent critic algorithm. In this paper, we introduce a novel utility function and propose a Q -function based policy iteration algorithm to eliminate the final tracking error. In addition, neural networks are used as function approximator to approximate the performance index and control policy. Considering the impact of the approximation error on the tracking performance, an approximation error bound for each iteration of the novel Q -function is established. Under the given conditions, the approximation Q -function converges to the finite neighborhood of the optimal value. Moreover, it is proved that weight estimation errors of neural networks are uniformly ultimately bounded. Finally, the effectiveness of the algorithm is verified by the simulation example.

关键词:

Optimal tracking control Policy iteration Neural networks Approximation errors Model-free control Adaptive dynamic programming

作者机构:

  • [ 1 ] [Gao, Ning]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Wang, Ding]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Zhao, Mingming]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Hu, Lingzhi]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Gao, Ning]Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intelligen, Beijing 100124, Peoples R China
  • [ 6 ] [Zhao, Mingming]Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intelligen, Beijing 100124, Peoples R China
  • [ 7 ] [Hu, Lingzhi]Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intelligen, Beijing 100124, Peoples R China
  • [ 8 ] [Gao, Ning]Beijing Univ Technol, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 9 ] [Zhao, Mingming]Beijing Univ Technol, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 10 ] [Hu, Lingzhi]Beijing Univ Technol, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 11 ] [Gao, Ning]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing 100124, Peoples R China
  • [ 12 ] [Zhao, Mingming]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing 100124, Peoples R China
  • [ 13 ] [Hu, Lingzhi]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing 100124, Peoples R China

通讯作者信息:

  • 王鼎

    [Wang, Ding]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

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来源 :

NEUROCOMPUTING

ISSN: 0925-2312

年份: 2024

卷: 572

6 . 0 0 0

JCR@2022

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ESI高被引论文在榜: 0 展开所有

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