A bridgeless Luo converter based speed control of switched reluctance motor using Particle Swarm Optimization (Pso) tuned proportional integral (Pi) controller

2020 ◽  
Vol 75 ◽  
pp. 103039 ◽  
Author(s):  
R. Kalai Selvi ◽  
R. Suja Mani Malar
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Speed Control ◽  
Switched Reluctance Motor ◽  
Pi Controller ◽  
Swarm Optimization ◽  
Switched Reluctance ◽  
Proportional Integral ◽  
Reluctance Motor

Adaptive nonlinear backstepping control using mended recurrent Romanovski polynomials neural network and mended particle swarm optimization for switched reluctance motor drive system

2019 ◽  
Vol 41 (14) ◽  
pp. 4114-4128 ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Switched Reluctance Motor ◽  
Drive System ◽  
Swarm Optimization ◽  
Switched Reluctance ◽  
Adaptive Law ◽  
Reluctance Motor ◽  
Nonlinear Backstepping Control

A switched reluctance motor (SRM) drive system has highly nonlinear uncertainties owing to a convex construction. It is hard for the linear control methods to achieve good performance for the SRM drive system. An adaptive nonlinear backstepping control system using the mended recurrent Romanovski polynomials neural network and mended PSO with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SRM drive system. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the mended recurrent Romanovski polynomials neural network and the error estimated law are established. Furthermore, to help improve convergence and to obtain better learning performance, the mended particle swarm optimization (PSO) algorithm is utilized for adjusting the two varied learning rates of the two parameters in the mended recurrent Romanovski polynomials neural network. Finally, some experimental results and a comparative analysis are verified that the proposed control scheme has better control performances for controlling the SRM drive system.

scholarly journals PMSM Speed Control Based on Particle Swarm Optimization and Deep Deterministic Policy Gradient under Load Disturbance

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 343
Author(s):  
Chiao-Sheng Wang ◽  
Chen-Wei Conan Guo ◽  
Der-Min Tsay ◽  
Jau-Woei Perng
Keyword(s):  
Particle Swarm Optimization ◽  
Permanent Magnet Synchronous Motor ◽  
Particle Swarm ◽  
Speed Control ◽  
Pso Algorithm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Estimation Equations ◽  
Policy Gradient

Proportional integral-based particle swarm optimization (PSO) and deep deterministic policy gradient (DDPG) algorithms are applied to a permanent-magnet synchronous motor to track speed control. The proposed methods, based on notebooks, can deal with time delay challenges, imprecise mathematical models, and unknown disturbance loads. First, a system identification method is used to obtain an approximate model of the motor. The load and speed estimation equations can be determined using the model. By adding the estimation equations, the PSO algorithm can determine the sub-optimized parameters of the proportional-integral controller using the predicted speed response; however, the computational time and consistency challenges of the PSO algorithm are extremely dependent on the number of particles and iterations. Hence, an online-learning method, DDPG, combined with the PSO algorithm is proposed to improve the speed control performance. Finally, the proposed methods are implemented on a real platform, and the experimental results are presented and discussed.

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