Adaptive Super-Twisting Speed Control for PMLSM system Using Generalized Proportional Integral Observer

2021 ◽  
Author(s):  
Honbin Cui ◽  
Qingfang Teng ◽  
Ruiqi Xu
Keyword(s):  
Speed Control ◽  
Proportional Integral

scholarly journals Speed control of brushless direct current motor using a genetic algorithm–optimized fuzzy proportional integral differential controller

2019 ◽  
Vol 11 (11) ◽  
pp. 168781401989019 ◽  
Author(s):  
Huangshui Hu ◽  
Tingting Wang ◽  
Siyuan Zhao ◽  
Chuhang Wang
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Direct Current ◽  
Fuzzy Logic Controller ◽  
Speed Control ◽  
Operating Conditions ◽  
Superior Performance ◽  
Membership Functions ◽  
Proportional Integral ◽  
Differential Type

In this article, a genetic algorithm–based proportional integral differential–type fuzzy logic controller for speed control of brushless direct current motors is presented to improve the performance of a conventional proportional integral differential controller and a fuzzy proportional integral differential controller, which consists of a genetic algorithm–based fuzzy gain tuner and a conventional proportional integral differential controller. The tuner is used to adjust the gain parameters of the conventional proportional integral differential controller by a new fuzzy logic controller. Different from the conventional fuzzy logic controller based on expert experience, the proposed fuzzy logic controller adaptively tunes the membership functions and control rules by using an improved genetic algorithm. Moreover, the genetic algorithm utilizes a novel reproduction operator combined with the fitness value and the Euclidean distance of individuals to optimize the shape of the membership functions and the contents of the rule base. The performance of the genetic algorithm–based proportional integral differential–type fuzzy logic controller is evaluated through extensive simulations under different operating conditions such as varying set speed, constant load, and varying load conditions in terms of overshoot, undershoot, settling time, recovery time, and steady-state error. The results show that the genetic algorithm–based proportional integral differential–type fuzzy logic controller has superior performance than the conventional proportional integral differential controller, gain tuned proportional integral differential controller, conventional fuzzy proportional integral differential controller, and scaling factor tuned fuzzy proportional integral differential controller.

scholarly journals Comparative Study of the Conventional Mathematical and Fuzzy Logic Controllers for Velocity Regulation

Axioms ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 53 ◽  
Author(s):  
Fevrier Valdez ◽  
Oscar Castillo ◽  
Camilo Caraveo ◽  
Cinthia Peraza
Keyword(s):  
Fuzzy Logic ◽  
Comparative Study ◽  
Speed Control ◽  
Raspberry Pi ◽  
Proportional Integral ◽  
Digital Era ◽  
Processing Information ◽  
Technology Market ◽  
Design Integration ◽  
Mindstorms Ev3

Currently, we are in the digital era, where robotics, with the help of the Internet of Things (IoT), is exponentially advancing, and in the technology market we can find multiple devices for achieving these systems, such as the Raspberry Pi, Arduino, and so on. The use of these devices makes our work easier regarding processing information or controlling physical mechanisms, as some of these devices have microcontrollers or microprocessors. One of the main challenges in speed control applications is to make the decision to use a fuzzy logic control (FLC) system instead of a conventional controller system, such as a proportional integral (PI) or a proportional integral-derivative (PID). The main contribution of this paper is the design, integration, and comparative study of the use of these three types of controllers—FLC, PI, and PID—for the speed control of a robot built using the Lego Mindstorms EV3 kit. The root mean square error (RMSE) and the settling time were used as metrics to validate the performance of the speed control obtained with the controllers proposed in this paper.

scholarly journals Robust ANFIS Vector Control of Induction Motor Drive for High-Performance Speed Control Supplied by a Photovoltaic Generator

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Vector Control ◽  
Induction Motor ◽  
High Performance ◽  
Fuzzy Inference ◽  
Speed Control ◽  
Inference System ◽  
Proportional Integral ◽  
Rotor Resistance ◽  
Resistance Variation ◽  
Drive Systems

This paper presents the vector control of Induction Motor (IM) supplied by a photovoltaic generatorwhich is controlled by an adaptive Proportional-Integral (PI) speed controller. The proposed solution is used toovercome the induction motor rotor resistance variation problem, which can affect negatively the performanceof the speed control. To overcome the rotor resistance variation, an adaptive Proportional-Integral controller isdeveloped with gains adaptation based on Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to guaranteea high performances of electric drive systems against the parametric variations. The proposed control algorithmis tested by Matlab-Simulink. Analysis of the obtained results shows the characteristic robustness to disturbancesof the load torque and to rotor resistance variation compared to the classical PI control and Model ReferenceAdaptive System (MRAS) rotor resistance observers.

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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