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作者:

Wang, Yuan (Wang, Yuan.) | Wang, Ding (Wang, Ding.) | Zhao, Mingming (Zhao, Mingming.) | Liu, Ao (Liu, Ao.) | Qiao, Junfei (Qiao, Junfei.)

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EI Scopus SCIE

摘要:

In this article, an accelerated Q -learning algorithm with evolving control is established to solve the optimal tracking control problem. First, an accelerated Q -learning scheme is constructed with an advanced Q -function. By utilizing the advanced Q -function, calculating of the feedforward control input can be avoided and the terminal tracking error can be eliminated. Then, by introducing the relaxation factor, the convergence rate of the iterative Q -function sequence is accelerated significantly, which is a potential way to diminish the computational burden. Furthermore, the convergence, positive definiteness, and stability conditions of the accelerated Q -learning algorithm are analyzed with some preconditions of the relaxation factor. Thus, the developed algorithm can achieve evolving control. Finally, the fantastic performance of the developed algorithm with critic network implementation is verified through two simulation examples.

关键词:

Stability analysis Nonlinear systems Adaptive dynamic programming Accelerated convergence rate Neural tracking control Q-learning

作者机构:

  • [ 1 ] [Wang, Yuan]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Wang, Yuan]Beijing Univ Technol, Key Lab Computat Intelligence & Intelligent Syst, Beijing 100124, Peoples R China
  • [ 3 ] [Wang, Ding]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Wang, Ding]Beijing Univ Technol, Key Lab Computat Intelligence & Intelligent Syst, Beijing 100124, Peoples R China
  • [ 5 ] [Qiao, Junfei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 6 ] [Qiao, Junfei]Beijing Univ Technol, Key Lab Computat Intelligence & Intelligent Syst, Beijing 100124, Peoples R China
  • [ 7 ] [Zhao, Mingming]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing 100124, Peoples R China
  • [ 8 ] [Liu, Ao]Beijing Univ Technol, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 9 ] [Qiao, Junfei]Beijing Univ Technol, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China

通讯作者信息:

  • [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: 584

6 . 0 0 0

JCR@2022

被引次数:

WoS核心集被引频次: 3

SCOPUS被引频次:

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 1

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