Separately Excited DC Motor Speed Tracking Control Using Adaptive Neuro-Fuzzy Inference System Based on Genetic Algorithm Particle Swarm Optimization and Fuzzy Auto-Tuning PID

3rd International Symposium on Resource Exploration and Environmental Science

生态环境科学

Elhaj Ahmed Mohamed, Mohamed^1^2 ; Guo, Yanling^1

College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin

150040, China^1

Karary University, Khartoum, Sudan^2

关键词: Adaptive neuro-fuzzy inference system;    Auto-tuning PID controllers;    Comparative studies;    Experimental conditions;    Genetic algorithm and particle swarm optimizations;    MATLAB/ SIMULINK;    Operation conditions;    Separately excited dc motors;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/300/4/042114/pdf
DOI  :  10.1088/1755-1315/300/4/042114

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

This paper presents detailed comparative studies to control the separately excited DC motor speed. The configurations of proposed methods are fuzzy auto-tuning PID and Adaptive Neuro-fuzzy inference system. The study intends to optimize the parameters of the transient speed response, for instance, the overshoot, settling time and rise time. Also, a comparison has been made between the two suggested controllers. PID tuned by Ziegler Nicholas's method utilized as a basis for the proposed controllers. Furthermore, PID model is used to extract the data set for ANFIS configuration, which has been manipulated by genetic algorithm and particle swarm optimization to ensure that it will be able to cover all operation condition possibilities. In order to investigate the eligibility of the suggested controllers, the model of the DC Motor is tested under several conditions. Finally, the results which implemented using Matlab-Simulink toolbox showed that fuzzy auto-tuning PID controller is too complicated, and has slow dynamic response comparing with ANFIS controller. Moreover, ANFIS has rapid robustness efficiency, also in the domain of the motor response characteristics. ANFIS expressed superior proficiency and better Keywords: SEDC motor, fuzzy auto-tuning PID, Adaptive Neuro-fuzzy inference system (ANFIS), genetic algorithm, particle swarm optimization, data set, and FIS. Performance under various experimental conditions.