基于MABSA-FLC和PSO-FLC的直流电机位置控制仿真比较

N. Elias, N. M. Yahya
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引用次数: 1

摘要

本文介绍了标准模糊控制器,并将其与改进的自适应蝙蝠声纳算法(MABSA)和粒子群优化算法(PSO)在仿真性能方面进行了比较。利用MATLAB工具箱设计模糊逻辑控制器(FLC)。在Simulink中对直流电动机进行建模,并将其转换为子系统,然后利用MATLAB工具箱对FLC进行设计。采用模糊控制器对直流电动机进行设计和仿真,并对差分算法进行优化,将作为模糊系统性能的基准。将Simulink示波器得到的结果与用于闭环系统动态响应的不同算法进行比较,并将带控制器和不带控制器的系统进行比较。诸如上升和沉降时间(以秒为单位)和最大超调(以百分比为单位)等参数将成为模拟结果的一部分。总体性能表明,与FLC和PSO-FLC相比,MABSA-FLC系统性能更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of DC Motor Position Control Simulation using MABSA-FLC and PSO-FLC
This paper explained about the standard fuzzy logic controller that will be compared in terms of performance for simulation with a modified adaptive bats sonar algorithm (MABSA) and also a particle swarm optimization (PSO) algorithm. A MATLAB toolbox is used to design the fuzzy logic controller (FLC). The DC motor was modeled, converted to a subsystem in Simulink and then the MATLAB toolbox is used to design the FLC. The methodology composed of the designing and also the simulation of DC motor with a fuzzy logic controller and optimization of the difference algorithm will be used as a benchmark for the performance of the fuzzy system. The results obtained from the Simulink scope are compared with the different algorithm used for the dynamic response of the closed-loop system and also the system with and without a controller will be compared. Parameters such as the rise and settling time in seconds and maximum overshoot in percent will be part of the simulation result. The overall performance shows that a system with MABSA-FLC performs well compared to the system with FLC and PSO-FLC.
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