scholarly journals Application of Adaptive PSO and Adaptive Fuzzy Logic Controllers to Speed Control PMSM Motor Servo Systems

2018 ◽  
Vol 220 ◽  
pp. 08003
Author(s):  
Le Dinh Hieu ◽  
Temkin Igor Olegovich
Keyword(s):  
Fuzzy Logic ◽  
Speed Control ◽  
Proportional Integral Derivative ◽  
Permanent Magnet Synchronous Motors ◽  
Servo Systems ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Adaptive Fuzzy ◽  
Adaptive Pso ◽  
Adaptive Fuzzy Logic

Three phases Permanent Magnet Synchronous Motors (PMSM) are non-linear resistors, resistance of stator winding, air gap flux, cross-coupling, saturation variable times and cogging torque in operation. Due to the nonlinear nature of PMSM, it is a challenge to control exactly the speed, torque and position. This paper presents two methods for speed control stabilization of the PMSM using the Adaptive Fuzzy Logic - Proportional Integral Derivative controller (AFL-PID) and Adaptive Particle Swarm Optimization - Proportional Integral Derivative controller (APSO-PID). The response results of the speed control PMSM Servo Systems use AFLC-PID and APSO-PID methods are compared and the conclusions are given.

A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system

2019 ◽  
Vol 26 (13-14) ◽  
pp. 1187-1198 ◽  
Author(s):  
Li-Xin Guo ◽  
Dinh-Nam Dao
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Deep Learning ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.

Comparative Study of Fuzzy PID and PID controller optimized with Spider Monkey Optimization for a Robotic Manipulator System

2019 ◽  
Vol 12 ◽  
Author(s):  
Alka Agrawal ◽  
Vishal Goyal ◽  
Puneet Mishra
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Optimization Technique ◽  
Absolute Error ◽  
Robotic Manipulator ◽  
Spider Monkey ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Spider Monkey Optimization

Background: Robotic manipulator system has been useful in many areas like chemical industries, automobile, medical fields etc. Therefore, it is essential to implement a controller for controlling the end position of a robotic armeffectively. However, with the increasing non-linearity and the complexities of a robotic manipulator system, a conventional Proportional-Integral-Derivative controller has become ineffective. Nowadays, intelligent techniques like fuzzy logic, neural network and optimization algorithms has emerged as an efficient tool for controlling the highly complex non-linear functions with uncertain dynamics. Objective: To implement an efficient and robustcontroller using Fuzzy Logic to effectively control the end position of Single link Robotic Manipulator to follow the desired trajectory. Methods: In this paper, a Fuzzy Proportional-Integral-Derivativecontroller is implemented whose parameters are obtainedwith the Spider Monkey Optimization technique taking Integral of Absolute Error as an objective function. Results: Simulated results ofoutput of the plants controlled byFuzzy Proportional-Integral-Derivative controller have been shown in this paper and the superiority of the implemented controller has also been described by comparing itwith the conventional Proportional-Integral-Derivative controller and Genetic Algorithm optimization technique. Conclusion: From results, it is clear that the FuzzyProportional-Integral-Derivativeoptimized with the Spider monkey optimization technique is more accurate, fast and robust as compared to the Proportional-Integral-Derivativecontroller as well as the controllers optimized with the Genetic algorithm techniques.Also, by comparing the integral absolute error values of all the controllers, it has been found that the controller optimized with the Spider Monkey Optimization technique shows 99% better efficacy than the genetic algorithm technique.

Position control and performance analysis of hydraulic system using two pump-controlling strategies

2018 ◽  
Vol 233 (9) ◽  
pp. 1093-1105 ◽  
Author(s):  
Gyan Wrat ◽  
Prabhat Ranjan ◽  
Mohit Bhola ◽  
Santosh Kumar Mishra ◽  
J Das
Keyword(s):  
Control Strategy ◽  
Position Control ◽  
Control Strategies ◽  
Speed Control ◽  
Hydraulic Systems ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Actuation System ◽  
Swash Plate

The role of hydraulic systems is quite evident especially in the case of heavy machineries employed in industries, where the utilisation of high forces amid large stiffness is the prerequisite. Nevertheless, there has been substantial effort put forward in the development of advanced control strategies which finally addressed the issue of the position control. Proportional–integral–derivative control strategy happens to be one among them, which is a versatile and widely renowned approach involved in the position control in this study. Although, it is quite frequently observed that the hydraulic actuation system possesses strong nonlinearities. In this article, two different actuator position control strategies, that is, swash plate control of main pump and speed control strategy of prime mover are compared. In swash plate control strategy, the proportional–integral–derivative controller adjusts the swash plate of main pump through servo mechanism, whereas in the speed control strategy, the proportional–integral–derivative controller adjusts the speed of the electric motor through variable-frequency drive. For this purpose, two MATLAB-Simulink models are developed and validated experimentally. It is found that swash plate control strategy has better dynamic and control performance than the speed control strategy catering same position demand of the linear actuator.

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