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Comparison of DC Motor Position Control Simulation using MABSA-FLC and PSO-FLC

机译:使用MABSA-FLC和PSO-FLC的直流电动机位置控制仿真的比较

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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.
机译:本文介绍了有关标准模糊逻辑控制器的信息,这些标准模糊控制器将在性能上进行比较,同时使用改进的自适应蝙蝠声纳算法(MABSA)和粒子群优化(PSO)算法。 MATLAB工具箱用于设计模糊逻辑控制器(FLC)。建模了直流电动机,将其转换为Simulink中的子系统,然后使用MATLAB工具箱设计FLC。由模糊逻辑控制器的直流电动机的设计,仿真以及差分算法的优化组成的方法学将被用作模糊系统性能的基准。从Simulink示波器获得的结果将与用于闭环系统动态响应的不同算法进行比较,还将对带有或不带有控制器的系统进行比较。诸如上升和建立时间(以秒为单位)和最大过冲(以百分比为单位)之类的参数将成为仿真结果的一部分。总体性能显示,与具有FLC和PSO-FLC的系统相比,具有MABSA-FLC的系统表现良好。

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