Fractional-Order Control Method Based on Twin-Delayed Deep Deterministic Policy Gradient Algorithm
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...
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| Published in | Fractal and fractional Vol. 8; no. 2; p. 99 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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01.02.2024
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| ISSN | 2504-3110 2504-3110 |
| DOI | 10.3390/fractalfract8020099 |
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| Abstract | 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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| AbstractList | 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. |
| Audience | Academic |
| Author | An, Zhengcai Jiao, Guangxin Sun, Dong Shao, Shuyi |
| Author_xml | – sequence: 1 givenname: Guangxin surname: Jiao fullname: Jiao, Guangxin – sequence: 2 givenname: Zhengcai surname: An fullname: An, Zhengcai – sequence: 3 givenname: Shuyi orcidid: 0000-0001-9458-661X surname: Shao fullname: Shao, Shuyi – sequence: 4 givenname: Dong surname: Sun fullname: Sun, Dong |
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| SubjectTerms | Algorithms Approximation Closed loops Control methods Controllers Design Disturbance observers Eigenvalues Feedback control FODOB FOSMC Machine learning Mathematical functions Methods Neural networks Optimization Radial basis function radial basis function network Signal processing Simulation Sliding mode control TD3 algorithm |
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| Title | Fractional-Order Control Method Based on Twin-Delayed Deep Deterministic Policy Gradient Algorithm |
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