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作者:

Hu, Qinna (Hu, Qinna.) | Wang, Ding (Wang, Ding.) | Liu, Ao (Liu, Ao.)

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EI Scopus

摘要:

In this paper, robust control problems are investigated for nonlinear continuous-time systems. A momentum-based gradient descent (GD) approach is developed to enhance the convergence performance of parameters in adaptive dynamic programming (ADP). By introducing the idea of momentum, the oscillation in the process of GD is alleviated and the selection of the learning rate becomes more flexible. Under the framework of ADP, the robust control problem is transformed into the optimal control problem by modifying the cost function. To avoid limitations of the initial admissible condition, an additional term is employed in the computation of the current gradient. Based on the online policy iteration algorithm, the momentum-based GD approach is constructed as an improved learning algorithm to optimize the critic network weights. Finally, a simulation is conducted to verify the effectiveness of the established learning strategy. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词:

Nonlinear programming Robustness (control systems) Nonlinear control systems Cost functions Adaptive control systems Continuous time systems Dynamic programming Optimal control systems Robust control

作者机构:

  • [ 1 ] [Hu, Qinna]Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 2 ] [Hu, Qinna]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Hu, Qinna]Beijing University of Technology, Beijing Laboratory of Smart Environmental Protection, Beijing; 100124, China
  • [ 4 ] [Wang, Ding]Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 5 ] [Wang, Ding]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China
  • [ 6 ] [Wang, Ding]Beijing University of Technology, Beijing Laboratory of Smart Environmental Protection, Beijing; 100124, China
  • [ 7 ] [Liu, Ao]Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 8 ] [Liu, Ao]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China
  • [ 9 ] [Liu, Ao]Beijing University of Technology, Beijing Laboratory of Smart Environmental Protection, Beijing; 100124, China

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ISSN: 1934-1768

年份: 2024

页码: 2432-2437

语种: 英文

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