基于频域优化算法的分数阶忆阻器建模与控制研究

IF 0.5 Q4 AUTOMATION & CONTROL SYSTEMS
Lanfeng Chen, Dinyyu Xue, Xinshu Cui
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引用次数: 0

摘要

分数阶忆阻器以其独特的存储特性、非线性特性和纳米级结构,在许多领域具有广泛的应用价值。因此成为近年来的研究热点。首先,分析了分数阶记忆电阻器在时域上随阶数变化的电导率波形和电压电流特性曲线。然后,基于粒子群优化算法结合Matlab函数fminsearch(),在频域识别分数阶忆阻器的传递函数模型。通过拟合曲线和目标函数值,验证了该方法的识别效果。最后,针对分数阶忆阻器模型设计了分数阶最优\(P{{I}^{\lambda }}{{D}^{\mu }}\)控制器。通过对指标的控制,证明了其控制效果远优于整阶PID控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Modeling and Control Research of Fractional-Order Memristor Based on Optimization Algorithm in Frequency Domain

Modeling and Control Research of Fractional-Order Memristor Based on Optimization Algorithm in Frequency Domain

Modeling and Control Research of Fractional-Order Memristor Based on Optimization Algorithm in Frequency Domain

Based on its unique memory characteristics, nonlinear characteristics, and nanoscale structure, fractional-order memristor has broad application value in many fields. So it has become a research hotspot in recent years. Firstly, this paper analyzed the waveforms of conductivity and voltage-current characteristic curves of fractional-order memristors with order changed in the time domain. Then, based on the particle swarm optimization algorithm combined with the Matlab function fminsearch(), the transfer function model of the fractional-order memristor was identified in the frequency domain. The identification effect was demonstrated to be good by fitting the curves and the value of the objective function. Finally, a fractional-order optimum \(P{{I}^{\lambda }}{{D}^{\mu }}\) controller was designed for the fractional-order memristor model. By controlling indicators, it is demonstrated that the control effect is much better than integer-order PID control.

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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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