Fractional-Order Control Method Based on Twin-Delayed Deep Deterministic Policy Gradient Algorithm

被引：0
|
作者：
Jiao, Guangxin [1 ]
An, Zhengcai [1 ]
Shao, Shuyi [1 ]
Sun, Dong [2 ]
机构：
[1] Nanjing Univ Aeronaut & Astronaut NUAA, Coll Automat Engn, Nanjing 211106, Peoples R China
[2] Jincheng Nanjing Engn Inst Aircraft Syst, Nanjing 211199, Peoples R China
来源：
FRACTAL AND FRACTIONAL | 2024年 / 8卷 / 02期
关键词：
FODOB; FOSMC; radial basis function network; TD3; algorithm;
D O I：
10.3390/fractalfract8020099
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
In this paper, a fractional-order control method based on the twin-delayed deep deterministic policy gradient (TD3) algorithm in reinforcement learning is proposed. A fractional-order disturbance observer is designed to estimate the disturbances, and the radial basis function network is selected to approximate system uncertainties in the system. Then, a fractional-order sliding-mode controller is constructed to control the system, and the parameters of the controller are tuned using the TD3 algorithm, which can optimize the control effect. The results show that the fractional-order control method based on the TD3 algorithm can not only improve the closed-loop system performance under different operating conditions but also enhance the signal tracking capability.
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页数：17
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