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Optimal Control of Complex Systems Based on Improved Dual Heuristic Dynamic Programming Algorithm

机译：基于改进的双重启发式动态规划算法的复杂系统最优控制

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摘要

When applied to solving the data modeling and optimal control problems of complex systems, the dual heuristic dynamic programming (DHP) technique, which is based on the BP neural network algorithm (BP-DHP), has difficulty in prediction accuracy, slow convergence speed, poor stability, and so forth. In this paper, a dual DHP technique based on Extreme Learning Machine (ELM) algorithm (ELM-DHP) was proposed. Through constructing three kinds of network structures, the paper gives the detailed realization process of the DHP technique in the ELM. The controller designed upon the ELM-DHP algorithm controlled a molecular distillation system with complex features, such as multivariability, strong coupling, and nonlinearity. Finally, the effectiveness of the algorithm is verified by the simulation that compares DHP and HDP algorithms based on ELM and BP neural network. The algorithm can also be applied to solve the data modeling and optimal control problems of similar complex systems.
机译：当用于解决复杂系统的数据建模和最优控制问题时，基于BP神经网络算法（BP-DHP）的双重启发式动态规划（DHP）技术难以预测精度，收敛速度慢，稳定性差等等。本文提出了一种基于极限学习机（ELM）算法（ELM-DHP）的双重DHP技术。通过构建三种网络结构，给出了ELM中DHP技术的详细实现过程。基于ELM-DHP算法设计的控制器控制着具有多种功能（如多变量，强耦合和非线性）的分子蒸馏系统。最后，通过比较基于ELM和BP神经网络的DHP和HDP算法的仿真，验证了算法的有效性。该算法还可用于解决类似复杂系统的数据建模和最优控制问题。

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  • 来源
    《Mathematical Problems in Engineering》 |2017年第9期|5476415.1-5476415.11|共11页
  • 作者

    Li Hui; Wen Yongsui; Sun Wenjie;

  • 作者单位

    Changchun Univ Technol, Sch Elect & Elect Engn, Changchun, Jilin, Peoples R China;

    Changchun Univ Technol, Sch Elect & Elect Engn, Changchun, Jilin, Peoples R China;

    Changchun Univ Technol, Sch Elect & Elect Engn, Changchun, Jilin, Peoples R China;

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