ABSTRACT

This paper proposes a Distance and Levy-flight based Crow Search Algorithm (DLCSA) for optimization of three Fractional-order controllers: Fractional Order Proportional-Integral-Derivative (FOPID), Fractional Order Proportional-Integral (FOPI) and Fractional Order Proportional-Derivative (FOPD) controllers. Novelties of the proposed method are fourfold; (a) distance-based Flight length (Dfl)to improve the convergence rate, (b) Levy-flight based evasion to improve the efficiency of the algorithm, (c)adaptive awareness probability(AP) to set a tradeoff between local and global search and (d) Fuzzy based adaptive objective function to adjust the solution weights automatically. With such adaptations, the new approach preserves solution diversity and improves the convergence to global optima. To demonstrate the effectiveness of the contributions, proposed methods: DLCSA-FOPID, DLCSA-FOPI, and DLCSA-FOPD are implemented on an Automatic Voltage Regulator system and results are compared with few other well-established techniques like Zeigler–Nichols (Z-N), Artificial Bee Colony (ABC) Optimization (ACO), Multi-Objective Extremal Optimization (MOEO), Genetic Algorithm (GA), Chaos Ant Swarm (CAS) algorithm, Real Coded Extremal Optimization (RCEO), etc. Further to investigate robustness of proposed methods, the results are obtained for set-point tracking, noise suppression, load disturbance rejection, control effort, and modeling errors. The results demonstrate the better performance of the proposed method as compared to other state of the art techniques.

摘要

本文提出了一种基于距离和征费飞行的乌鸦搜索算法 (DLCSA)，用于优化三个分数阶控制器：分数阶比例-积分-导数 (FOPID)、分数阶比例-积分 (FOPI) 和分数阶比例-导数(FOPD) 控制器。所提出的方法的新颖性有四点；(a)基于距离的飞行长度( Dfl )提高收敛速度，(b)基于Levy-flight的规避，提高算法的效率，(c)自适应感知概率( AP）在局部和全局搜索之间进行权衡，以及（d）基于模糊的自适应目标函数自动调整解决方案权重。通过这种适应，新方法保留了解决方案的多样性并提高了对全局最优的收敛性。为了证明贡献的有效性，提出的方法：DLCSA-FOPID、DLCSA-FOPI 和 DLCSA-FOPD 在自动电压调节器系统上实施，并将结果与​​ Zeigler-Nichols (ZN) 等少数其他成熟技术进行了比较，人工蜂群（ABC）优化（ACO）、多目标极值优化（MOEO）、遗传算法（GA）、混沌蚁群（CAS）算法、实编码极值优化（RCEO）等。进一步研究提出的鲁棒性方法，获得的结果用于设定点跟踪，噪声抑制，负载扰动抑制、控制工作和建模错误。结果表明，与其他现有技术相比，所提出的方法具有更好的性能。