Design Fractional-order PID Controllers for Single-Joint Robot Arm Model

Q3 Computer Science

International Journal of Advances in Soft Computing and its Applications Pub Date : 2022-07-20 DOI:10.15849/ijasca.220720.07

Iqbal M. Batiha, Suhaib A. Njadat, Radwan M. Batyha, A. Zraiqat, Amer Dababneh, S. Momani

引用次数: 10

Abstract

he major goal of the this work is to present an optimal design of the Fractional-order Proportional-Derivative-Integral (FoPID) controller for the single-joint arm dynamics. For meeting this aim, the Particle Swarm Optimization (PSO) algorithm will be implement to tune the parameters of such controller. Six FoPID-controllers will be generated in accordance with two kinds of approaches (Continued Fraction Expansion (CFE) and Outstaloup’s approaches) for Laplacian operators, coupled with three fitness functions (IAE, ITAE, ITSE). These controllers will be competed to each other to determine which one can provide to the closed-loop system of the single-joint robot arm model a good rise time, short settling time, and an excellent overshoot. Keywords: Fractional-order model; Oustaloup approximation, Continued

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单关节机械臂模型的分数阶PID控制器设计

本文的主要目标是提出单关节臂动力学的分数阶比例导数积分(FoPID)控制器的优化设计。为了实现这一目标，将采用粒子群优化算法(PSO)对该控制器的参数进行整定。根据拉普拉斯算子的两种方法(连分数展开法(CFE)和Outstaloup方法)，结合3种适应度函数(IAE, ITAE, ITSE)，生成6个fopid控制器。这些控制器将相互竞争，以确定哪一个可以为单关节机械臂模型的闭环系统提供良好的上升时间，短的沉降时间和良好的超调量。关键词:分数阶模型;乌斯塔鲁近似，继续

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来源期刊

International Journal of Advances in Soft Computing and its Applications Computer Science-Computer Science Applications

CiteScore

3.30

自引率

0.00%

发文量

期刊介绍： The aim of this journal is to provide a lively forum for the communication of original research papers and timely review articles on Advances in Soft Computing and Its Applications. IJASCA will publish only articles of the highest quality. Submissions will be evaluated on their originality and significance. IJASCA invites submissions in all areas of Soft Computing and Its Applications. The scope of the journal includes, but is not limited to: √ Soft Computing Fundamental and Optimization √ Soft Computing for Big Data Era √ GPU Computing for Machine Learning √ Soft Computing Modeling for Perception and Spiritual Intelligence √ Soft Computing and Agents Technology √ Soft Computing in Computer Graphics √ Soft Computing and Pattern Recognition √ Soft Computing in Biomimetic Pattern Recognition √ Data mining for Social Network Data √ Spatial Data Mining & Information Retrieval √ Intelligent Software Agent Systems and Architectures √ Advanced Soft Computing and Multi-Objective Evolutionary Computation √ Perception-Based Intelligent Decision Systems √ Spiritual-Based Intelligent Systems √ Soft Computing in Industry ApplicationsOther issues related to the Advances of Soft Computing in various applications.