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...

Full description

Saved in:
Bibliographic Details
Published inFractal and fractional Vol. 8; no. 2; p. 99
Main Authors Jiao, Guangxin, An, Zhengcai, Shao, Shuyi, Sun, Dong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2024
Subjects
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
Online AccessGet full text
ISSN2504-3110
2504-3110
DOI10.3390/fractalfract8020099

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2504-3110
2504-3110
DOI:10.3390/fractalfract8020099