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作者:

Yuan, Zeqiang (Yuan, Zeqiang.) | Wang, Ding (Wang, Ding.) | Liu, Ao (Liu, Ao.) | Hu, Qinna (Hu, Qinna.) | Zhou, Zihang (Zhou, Zihang.)

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摘要:

In this paper, we develop a decentralized tracking control (DTC) strategy to stabilize a class of continuous-time nonlinear interconnected large-scale systems in the presence of unmatched external disturbances. This strategy is derived from an online learning approach of optimal control. Initially, the DTC problem is formulated to ensure both tracking control of isolated subsystems and the overall stability of the large-scale system. By combining the tracking error with the reference trajectory, some augmented systems are constructed and then the DTC design is transformed into an optimal control problem. Considering external disturbances, we formulated it as a two-player zero-sum game. Critic neural networks are utilized to solve the Hamilton-Jacobi-Isaacs equation. This approach allows for the estimation of Nash equilibrium solution that encompasses both the optimal control law and the worst-case disturbance law. Notably, a novel gradient descent strategy with momentum is established to tune the weights of the critic neural network for approximating the cost function better. Finally, an experimental simulation is provided to verify the effectiveness of the developed DTC scheme. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词:

Large scale systems Nonlinear programming Reinforcement learning Nonlinear control systems Optimal control systems Decentralized systems Control theory Dynamic programming Cost functions Continuous time systems Adaptive control systems

作者机构:

  • [ 1 ] [Yuan, Zeqiang]Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 2 ] [Yuan, Zeqiang]Beijing University of Technology, Beijing Institute of Artificial Intelligence, Beijing; 100124, China
  • [ 3 ] [Yuan, Zeqiang]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 University of Technology, Beijing Institute of Artificial Intelligence, 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 University of Technology, Beijing Institute of Artificial Intelligence, Beijing; 100124, China
  • [ 9 ] [Liu, Ao]Beijing University of Technology, Beijing Laboratory of Smart Environmental Protection, Beijing; 100124, China
  • [ 10 ] [Hu, Qinna]Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 11 ] [Hu, Qinna]Beijing University of Technology, Beijing Institute of Artificial Intelligence, Beijing; 100124, China
  • [ 12 ] [Hu, Qinna]Beijing University of Technology, Beijing Laboratory of Smart Environmental Protection, Beijing; 100124, China
  • [ 13 ] [Zhou, Zihang]Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 14 ] [Zhou, Zihang]Beijing University of Technology, Beijing Institute of Artificial Intelligence, Beijing; 100124, China
  • [ 15 ] [Zhou, Zihang]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: 2456-2461

语种: 英文

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