利用遗传算法和粒子群优化对直流电机 PID 控制器的自动参数检测质量进行调查

Nhat Quang Dao
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摘要

本文介绍了通过遗传算法(GA)和粒子群优化(PSO)为直流电机选择最佳 PID 参数的研究结果。模拟控制器响应结果表明,PID - GA 和 PID - PSO 组合算法优于传统方法。该结果还允许选择最佳算法--结合 PSO - PID 来设计一个控制器,与 GA-PID 方法相比,该控制器具有较小的沉降误差,但过冲和沉降时间较大。仿真在 Matlab 环境中进行
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Survey on the quality of automatic parameter detection of PID controller for DC motor using Genetic Algorithm and Particle Swarm Optimization
This article presents the results of a study on selecting optimal PID parameters tuned by Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) used for a DC motor. The simulating controller response results show that the PID - GA and PID - PSO combination algorithms are superior to traditional methods. The result also allows for the selection of the optimal algorithm - combining the PSO - PID to design a controller that has smaller settling error but larger overshoot and settling time compared to GA-PID method. The simulation was taken in Matlab environments
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