Performance Enhancement of SMC Based Buck Converter under Variable Conditions by Particle Swarm Optimization Algorithm

S. Vadi, R. Bayindir
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引用次数: 0

Abstract

In case of load change at the output of the converters, the inductor and capacitor sizes in the converters should also change. Otherwise, there will be distortions in the dynamic response of the converter in terms of performance criteria such as settling time, rise time and maximum overshoot amount. This paper presents to improve the control's performances optimization using particle swarm optimization (PSO) of the system consisting of a buck converter based on sliding mode control (SMC) under variable conditions. Thus, a stable control structure has created by calculating the optimal values of the coefficients in the sliding mode control structure with the PSO method. The input source is taken as direct source (DC) source voltage at variable conditions. Buck Converter output voltage has been tested on variable loads. The modeling is done in MATLAB/Simulink software.
基于粒子群优化算法的变条件下SMC降压变换器性能增强
如果变流器输出端的负载发生变化,则变流器中的电感和电容器的尺寸也应发生变化。否则,在稳定时间、上升时间和最大超调量等性能指标方面,变换器的动态响应会出现失真。针对变条件下基于滑模控制(SMC)的buck变换器系统,提出了利用粒子群算法(PSO)改进控制性能的优化方法。因此,利用粒子群算法计算滑模控制结构中各系数的最优值,建立了稳定的控制结构。在可变条件下,将输入源作为直流源电压。Buck变换器的输出电压在可变负载下进行了测试。在MATLAB/Simulink软件中进行建模。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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