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
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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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