| 基于执行依赖启发式动态规划的船舶减摇鳍在线最优控制 |
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| 引用本文： | 阮光维,李铁山,于仁海,刘琪.基于执行依赖启发式动态规划的船舶减摇鳍在线最优控制[J].南京气象学院学报,2021,13(1):10-16. |
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| 作者姓名： | 阮光维 李铁山 于仁海 刘琪 |
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| 作者单位： | 大连海事大学 航海学院, 大连, 116026;越南海事大学 航海学院, 越南 海防, 180000,大连海事大学 航海学院, 大连, 116026;电子科技大学 自动化工程学院, 成都, 611731,大连海事大学 航海学院, 大连, 116026,大连海事大学 航海学院, 大连, 116026 |
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| 基金项目： | 国家自然科学基金（51939001，61976033）；大连市重点学科重大课题科技创新基金（2018J11CY022）；辽宁省兴辽英才计划高水平创新创业团队（XLYC1908018，XLYC1807046）；辽宁省自然科学基金（20180550082，2019-ZD-0151）；中央高校基本科研业务费项目（3132019345） |
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| 摘 要： | 针对船舶线性横摇系统,设计了一种基于执行依赖启发式动态规划（ADHDP）方法的在线学习最优减摇鳍控制器.在设计过程中直接使用输入输出数据获取系统状态值.利用评价网络来逼近针对船舶减摇鳍控制系统设计的性能指标函数,并通过执行网络获得最优控制律,这两个网络都是多层前馈神经网络,即反向传播（BP）神经网络.在训练过程中,这两个神经网络不仅可以使用实时测量数据,也可以减少船舶横摇模型的内部误差和不确定性干扰的影响,从而提高系统的鲁棒性.最后,仿真结果表明所提出的ADHDP控制器对于降低船舶横摇有很好的控制效果.
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| 关 键 词： | 执行依赖启发式动态规划(ADHDP) 自适应动态规划 船舶减摇鳍 最优控制 |
| 收稿时间： | 2020/11/1 0:00:00 |
Online optimal control for ship fin stabilizer system based on action dependent heuristic dynamic programming |
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| NGUYEN Quangduy,LI Tieshan,YU Renhai and LIU Qi.Online optimal control for ship fin stabilizer system based on action dependent heuristic dynamic programming[J].Journal of Nanjing Institute of Meteorology,2021,13(1):10-16. |
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| Authors: | NGUYEN Quangduy LI Tieshan YU Renhai and LIU Qi |
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| Institution: | Navigation College, Dalian Maritime University, Dalian 116026;Faculty of Navigation, Vietnam Maritime University, Haiphong 180000, Vietnam,Navigation College, Dalian Maritime University, Dalian 116026;School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731,Navigation College, Dalian Maritime University, Dalian 116026 and Navigation College, Dalian Maritime University, Dalian 116026 |
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| Abstract: | When ships are sailing on the sea,roll motion will greatly reduce the safety of ships and cargo,as well as the health of the crew.Therefore,the ship roll stabilization device has become one of the indispensable equipment on the ship.As an active roll reduction device,fin stabilizer is widely used for roll reduction due to its good anti-rolling performance.In this paper,an online learning optimal controller based on action dependent heuristic dynamic programming (ADHDP) is proposed for the ship fin stabilizer system.The input and output data,instead of a system model,are used in the design to obtain the system state.Two back propagation neural networks,including a critic network and an action network,are used to approximate the performance function and obtain the control law,respectively.The two neural networks can use real-time measurement data,and reduce internal model error and the uncertainty disturbance,thus improve the robustness of the system.Finally,the effectiveness of the proposed ADHDP controller is validated by simulation results. |
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| Keywords: | action dependent heuristic dynamic programming (ADHDP) adaptive dynamic programming ship fin stabilizer optimal control |
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