Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi

2147-5881

Pamukkale Üniversitesi

Electrical Engineering (Other)

Elektrik Mühendisliği (Diğer)

Effective fractional order PID control design of DC Motors using the walrus optimization algorithm

DC motorların mors optimizasyon algoritması kullanılarak etkili kesir dereceli PID kontrol tasarımı

Başak

Hasan

ARTVIN CORUH UNIVERSITY Doğan

Kadri

ARTVIN CORUH UNIVERSITY

08 25 2025

31 4 625 632 08 05 2024 12 08 2024

2013

Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi

This paper presents an approach utilizing the Walrus Optimization Algorithm (WaOA) to tune the parameters of the fractional-order proportional-integral-derivative (FOPID) controller for regulating the speed of direct current (DC) motors. The optimal controller settings are determined employing a time-based performance metric, serving as a cost function, to attain high performance. The success of the WaOA-based FOPID controller is demonstrated through comprehensive statistical analysis and simulations. The results indicate that the WaOA effectively find the optimal parameters of the FOPID controller. Moreover, the superiority of the WaOA-based FOPID-controlled DC motor system is further supported by a detailed time-domain analysis. The WaOA-FOPID controller exhibits superior performance when compared with controllers utilizing alternative algorithms such as leader-based harris hawks, chaotic artificial hummingbird, improved slime mould, manta ray foraging based on opposition and simulated annealing, reptile search, prairie dog, grey wolf, and atom search. According to the results, WaOA emerges as an efficient method for optimizing FOPID parameters in the context of speed control for DC motor systems.

Bu makale, doğru akım (DC) motorlarının hızını düzenlemek için kesirli mertebeden oransal-integral-türev (FOPID) denetleyicisinin parametrelerini ayarlamak için Mors Optimizasyon Algoritmasını (WaOA) kullanan bir yaklaşım sunmaktadır. Optimum kontrolör ayarları, yüksek performans elde etmek için maliyet fonksiyonu olarak hizmet veren zaman tabanlı bir performans ölçütü kullanılarak belirlenir. WaOA tabanlı FOPID kontrolörün başarısı, kapsamlı istatistiksel analiz ve simülasyonlarla gösterilmiştir. Sonuçlar, WaOA'nın FOPID kontrolörünün optimum parametrelerini etkili bir şekilde bulduğunu göstermektedir. Ayrıca, WaOA tabanlı FOPID kontrollü DC motor sisteminin üstünlüğü, ayrıntılı bir zaman alanı analizi ile daha da desteklenmektedir. WaOA-FOPID kontrolörü, lider tabanlı harris şahinleri, kaotik yapay sinek kuşu, iyileştirmiş cıvık mantar, karşıtlık ve tavlama benzetimine dayalı manta vatozu beslenme, sürüngen arama, çayır köpeği, gri kurt ve atom arama gibi alternatif algoritmalar kullanan kontrolörlerle karşılaştırıldığında üstün performans sergilemektedir. Sonuçlara göre, WaOA, DC motor sistemleri için hız kontrolü bağlamında FOPID parametrelerini optimize etmek için etkili bir yöntem olarak ortaya çıkmaktadır.

DC motor FOPID controller Walrus optimization algorithm Metaheuristics

DC motor FOPID denetleyici Mors optimizasyonu algoritması Metasezgisel

[1] Izci D, Ekinci S. “Fractional order controller design via gazelle optimizer for efficient speed regulation of micromotors”. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 6, 100295, 2023.

[2] Afifa R, Ali S, Pervaiz M, Iqbal J. “Adaptive backstepping integral sliding mode control of a MIMO separately excited DC motor”. Robotics, 12(4), 105, 2023.

[3] Izci D. “Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder-Mead algorithm”. Transactions of the Institute of Measurement and Control, 43(14), 3195-3211, 2021.

[4] Tepljakov A, Petlenkov E, Belikov J. “FOMCOM: a MATLAB toolbox for fractional-order system identification and control”. International Journal of Microelectronics and computer science, 2(2), 51-62, 2011.

[5] Hekimoğlu B. “Optimal tuning of fractional order PID controller for DC motor speed control via chaotic atom search optimization algorithm”. IEEE Access, 7, 38100-38114, 2019.

[6] Ayinla SL, Amosa TI, Ibrahim O, Rahman M S, Bahashwan AA, Mostafa MG, Yusuf AO. “Optimal control of DC motor using leader-based Harris Hawks optimization algorithm”. Franklin Open, 6, 100058, 2024.

[7] Ekinci S, Izci D, and Hekimoğlu B. “Optimal FOPID speed control of DC motor via opposition-based hybrid manta ray foraging optimization and simulated annealing algorithm”. Arabian Journal for Science and Engineering, 46(2), 1395-1409, 2021.

[8] Ekinci S, Hekimoğlu B, Izci D. “Opposition based Henry gas solubility optimization as a novel algorithm for PID control of DC motor”. Engineering Science and Technology, an International Journal, 24(2), 331-342, 2021.

[9] Widya Suseno E, Ma’arif A. “Tuning of PID controller parameters with genetic algorithm method on DC motor”. International Journal of Robotics and Control Systems, 1(1), 41-53, 2021.

[10] Patil MD, Vadirajacharya K, Khubalkar SW. “Design and tuning of digital fractional-order PID controller for permanent magnet DC motor”. IETE Journal of Research, 69(7), 4349-4359, 2023.

[11] Gupta N, Kaur M, Gupta R. “Ant colony optimization based optimal tuning of Fractional Order (FO) PID controller for controlling the speed of a DC motor”. Journal of Engineering Research, 11(3), 135-145, 2023.

[12] Gündoğdu, Ö. “Genetik algoritma kullanılarak PID kontrolcü kazançlarının optimum ayarlanması”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 11(1), 131-135, 2005.

[13] Rajendran A, Karthikeyan M, Saravanakumar G. “Implementation of FOPID controller with modified harmony search optimization for precise modelling and auto-tuning of nonlinear systems”. Automatika, 65(3), 881-893, 2024.

[14] Wolpert DH, Macready WG. “No free lunch theorems for optimization”. IEEE Transactions on Evolutionary Computation, 1(1), 67-82, 1997.

[15] Trojovský P, Dehghani M. “A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior”. Scientific Reports, 13(1), 1-32, 2023.

[16] Hasanah M, Putri RA, Putra MAR, Ahmad T. “Analysis of Weight-Based Voting Classifier for Intrusion Detection System“. International Journal of Intelligent Engineering and Systems, 17(2), 190-200, 2024.

[17] Hasanien HM, Alsaleh I, Ullah Z, Alassaf A. “Probabilistic optimal power flow in power systems with Renewable energy integration using Enhanced walrus optimization algorithm”. Ain Shams Engineering Journal, 15(3), 102663, 2024.

[18] Shaheen MA, Hasanien HM, Mekhamer SF, Talaat HE. “Walrus optimizer-based optimal fractional order PID control for performance enhancement of offshore wind farms”. Scientific Reports, 14(1), 17636, 2024.

[19] Gaing Z-L. “A particle swarm optimization approach for optimum design of PID controller in AVR system”. IEEE Transactions on Energy Conversion, 19(2), 384-391, 2004.

[20] Sarma H, Bardalai A. “Improvisation of artificial hummingbird algorithm through incorporation of chaos theory in intelligent optimization of fractional order PID controller tuning”. International Journal of Information Technology, 1-18, 2024.

[21] Izci D, Ekinci S, Zeynelgil HL, Hedley J. “Fractional order PID design based on novel improved slime mould algorithm”. Electric Power Components and Systems, 49(9-10), 901-918, 2021.

[22] Izci D, Ekinci S, Zeynelgil HL, Hedley J. “Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems”. Transactions of the Institute of Measurement and Control, 44(2), 435-456, 2022.

[23] Agarwal J, Parmar G, Gupta R, Sikander A. “Analysis of grey wolf optimizer based fractional order PID controller in speed control of DC motor”. Microsystem Technologies, 24(12), 4997-5006, 2018

[24] Shah P, Agashe S. “Review of fractional PID controller”. Mechatronics, 38, 29-41, 2016.

[25] Abualigah L, Elaziz MA, Sumari P, Geem ZW, Gandomi AH. “Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer”. Expert Systems with Applications, 191, 116158, 2022.

[26] Ezugwu AE, Agushaka JO, Abualigah L, Mirjalili S, Gandomi AH. “Prairie dog optimization algorithm”. Neural Computing and Applications, 34(22), 20017-20065, 2022.