MDPI - Publisher of Open Access Journals

20 pages, 9761 KiB

Open AccessArticle

Vector Control of PMSM Using TD3 Reinforcement Learning Algorithm

by Fengyuan Yin, Xiaoming Yuan, Zhiao Ma and Xinyu Xu

Algorithms 2023, 16(9), 404; https://doi.org/10.3390/a16090404 - 24 Aug 2023

Cited by 13 | Viewed by 4468

Permanent magnet synchronous motor (PMSM) drive systems are commonly utilized in mobile electric drive systems due to their high efficiency, high power density, and low maintenance cost. To reduce the tracking error of the permanent magnet synchronous motor, a reinforcement learning (RL) control algorithm based on double delay deterministic gradient algorithm (TD3) is proposed. The physical modeling of PMSM is carried out in Simulink, and the current controller controlling i_d-axis and i_q-axis in the current loop is replaced by a reinforcement learning controller. The optimal control network parameters were obtained through simulation learning, and DDPG, BP, and LQG algorithms were simulated and compared under the same conditions. In the experiment part, the trained RL network was compiled into C code according to the workflow with the help of rapid prototyping control, and then downloaded to the controller for testing. The measured output signal is consistent with the simulation results, which shows that the algorithm can significantly reduce the tracking error under the variable speed of the motor, making the system have a fast response. Full article

(This article belongs to the Special Issue Algorithms in Evolutionary Reinforcement Learning)

► Show Figures

Figure 1

19 pages, 9549 KiB

Open AccessArticle

Reinforcement Learning Control of Hydraulic Servo System Based on TD3 Algorithm

by Xiaoming Yuan, Yu Wang, Ruicong Zhang, Qiang Gao, Zhuangding Zhou, Rulin Zhou and Fengyuan Yin

Machines 2022, 10(12), 1244; https://doi.org/10.3390/machines10121244 - 19 Dec 2022

Cited by 15 | Viewed by 5347

Abstract

This paper aims at the characteristics of nonlinear, time-varying and parameter coupling in a hydraulic servo system. An intelligent control method is designed that uses self-learning without a model or prior knowledge, in order to achieve certain control effects. The control quantity can be obtained at the current moment through the continuous iteration of a strategy–value network, and the online self-tuning of parameters can be realized. Taking the hydraulic servo system as the experimental object, a twin delayed deep deterministic (TD3) policy gradient was used to reinforce the learning of the system. Additionally, the parameter setting was compared using a deep deterministic policy gradient (DDPG) and a linear–quadratic–Gaussian (LQG) based on linear quadratic Gaussian objective function. To compile the reinforcement learning algorithm and deploy it to the test platform controller for testing, we used the Speedgoat prototype target machine as the controller to build the fast prototype control test platform. MATLAB/Coder and compute unified device architecture (CUDA) were used to generate an S-function. The results show that, compared with other parameter tuning methods, the proposed algorithm can effectively optimize the controller parameters and improve the dynamic response of the system when tracking signals. Full article

(This article belongs to the Topic Designs and Drive Control of Electromechanical Machines)

► Show Figures

Figure 1

12 pages, 1776 KiB

Open AccessArticle

Diffusion Generalized MCC with a Variable Center Algorithm for Robust Distributed Estimation

by Wentao Ma, Panfei Cai, Fengyuan Sun, Xiao Kou, Xiaofei Wang and Jianning Yin

Electronics 2021, 10(22), 2807; https://doi.org/10.3390/electronics10222807 - 16 Nov 2021

Cited by 2 | Viewed by 2016

Abstract

Classical adaptive filtering algorithms with a diffusion strategy under the mean square error (MSE) criterion can face difficulties in distributed estimation (DE) over networks in a complex noise environment, such as non-zero mean non-Gaussian noise, with the object of ensuring a robust performance. In order to overcome such limitations, this paper proposes a novel robust diffusion adaptive filtering algorithm, which is developed by using a variable center generalized maximum Correntropy criterion (GMCC-VC). Generalized Correntropy with a variable center is first defined by introducing a non-zero center to the original generalized Correntropy, which can be used as robust cost function, called GMCC-VC, for adaptive filtering algorithms. In order to improve the robustness of the traditional MSE-based DE algorithms, the GMCC-VC is used in a diffusion adaptive filter to design a novel robust DE method with the adapt-then-combine strategy. This can achieve outstanding steady-state performance under non-Gaussian noise environments because the GMCC-VC can match the distribution of the noise with that of non-zero mean non-Gaussian noise. The simulation results for distributed estimation under non-zero mean non-Gaussian noise cases demonstrate that the proposed diffusion GMCC-VC approach produces a more robustness and stable performance than some other comparable DE methods. Full article

(This article belongs to the Section Microwave and Wireless Communications)

► Show Figures

Figure 1

Search Results (3)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI

Saved Queries

Search Filter Reset All

Years

Feature Papers

Subjects

Journals

Article Types

Countries / Regions

Search Results (3)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI