Performance Analysis of Error-Based and User-Defined Objective Functions for a Particle Swarm Optimization Tuned PID Controller with Derivative Filter

Yıl 2019, Cilt: 19 Sayı: 3, 682 - 689, 31.12.2019

Erdinç Şahin , Mustafa Şinasi Ayas

https://doi.org/10.35414/akufemubid.520823

Cited By: 2

Öz

In this study, we
investigate the performance analysis of transient and steady state characteristics
of the commonly used error-based objective functions (EBOF) such as integral of
squared error (ISE), integral of time weighted squared error (ITSE), integral
of absolute error (IAE), and integral of time weighted absolute error (ITAE) and
a user-defined objective function (UDOF). In optimization process, particle
swarm optimization (PSO) algorithm tuned proportional-integral-derivative controller
with derivative filter (PIDF) is employed for a second order plus dead time
(SOPDT) test system. Simulation results shows the superiority of the UDOF in
terms of settling time, overshoot, and settling minimum value compared to EBOFs.

Anahtar Kelimeler

Error-based objective functions, User-defined objective functions, Classical PID controller, PSO algorithm, Transient and steady state response analysis

Parçacık Sürü Optimizasyonu Ayarlı Türev Etkisi Filtreli Bir PID Denetleyici için Hata Tabanlı ve Kullanıcı Tanımlı Amaç Fonksiyonlarının Performans Analizi

Öz

Bu
çalışmada, hatanın karesinin integrali (HKİ), zaman ağırlıklı hatanın karesinin
integrali (ZAHKİ), mutlak hatanın integrali (MHİ) ve zaman ağırlıklı mutlak
hatanın integrali (ZAMHİ) gibi control sistemleri tasarımında sık kullanılan
hata tabanlı amaç fonksiyonları (HTAF) ile kullanıcı tanımlı amaç
fonksiyonlarının (KTAF) geçici ve kalıcı durum tepkilerinin performans analizi
incelenmiştir. Optimizasyon sürecinde, parçacık sürüsü optimizasyonu (PSO)
algoritması tarafından ayarlanan türev etkisi filtreli oransal-integral-türevsel
denetleyici, ikinci dereceden ölü zamanlı bir test sistemi için kullanılmıştır.
Simülasyon sonuçları, KTAF'IN oturma zamanı, aşım ve alt aşım değerlerindeki
üstünlüğünü göstermektedir.

Anahtar Kelimeler

Hata tabanlı amaç fonksiyonları, Kullanıcı tanımlı amaç fonksiyonları, Klasik PID denetleyici, PSO algoritması, Kalıcı ve geçici durum analizi

Kaynakça

• Ang, K. H., Chong, G. and Li, Y., 2005. PID control system analysis, design, and technology. IEEE Transactions on Control Systems Technology, 13,559-576.
• Åström, K. J. and Hägglund, T., 2001. The future of PID control. Control Engineering Practice, 9, 1163-1175.
• Deepyaman, M., Ayan, A., Mithun C., Amit K. and Ramdoss, J., 2008. Tuning PID and PIλDμ controllers using the integral time absolute error criteria. International Conference on Information and Automation for Sustainability (ICIAFS), 457-462.
• Eberhart, R.C. and Shi, Y., 2001. Particle swarm optimization: developments, applications and resources. IEEE Proceedings of the Evolutionary Computation Congress, 1, 81-86.
• Gaing, Z. L., 2004. A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Transactions on Energy Conversion, 19, 384-91.
• Giriraj Kumar, S. M., Jayaraj D. and Kishan A. R., 2010. PSO based tuning of a PID controller for a high performance drilling machine. International Journal of Computer Applications, 1, 12-18.
• Itik, M., Sahin, E. and Ayas M. S., 2015. Fractional order control of conducting polymer artificial muscles. Expert System with Applications, 42, 8212-20.
• Johnson M. A. and Moradi M. H., 2006. PID control: new identification and design methods, Springer Science & Business Media, 7-9.
• Karasakal, O., Yeşil E., Güzelkaya M. and Eksin I., 2005. Implementation of a new self-tuning fuzzy PID controller on PLC. Turkish Journal of Electrical Engineering & Computer Sciences, 13, 277-286.
• Kennedy J. and Eberhart R., 1995. Particle swarm optimization. IEEE International Conference on Neural Networks, 1942-1948.
• Latha K., Rajinikanth V. and Surekha P. M., 2013. PSO-based PID controller design for a class of stable and unstable systems. ISRN Artificial Intelligence, 2013, 1-11.
• Lee K. S. and Geem Z. W., 2005. A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer methods in applied mechanics and engineering, 23, 3902-33.
• Nagaraj B. and Murugananth N., 2010. A comparative study of PID controller tuning using GA, EP, PSO and ACO. IEEE International Conference in Communication Control and Computing Technologies (ICCCCT), 305-313.
• Poli R., 2008. Analysis of the publications on the applications of particle swarm optimization. Journal of Artificial Evolution and Applications, 2008, 1-10.
• Sharaf A. M. and El-Gammal A. A., 2009. An integral squared error-ISE optimal parameters tuning of modified PID controller for industrial PMDC motor based on Particle Swarm Optimization-PSO. IEEE International Conference in Power Electronics and Motion Control (IPEMC), 1953-1959.
• Štimac, G. Braut S.and Žigulić R., 2014. Comparative analysis of PSO algorithms for PID controller tuning. Chinese Journal of Mechanical Engineering, 27, 928-36.
• Wang Barnes L. T. J. D. and Cluett W. R., 1995. New frequency-domain design method for PID controllers. IEE Proceedings-Control Theory and Applications, 142, 265–271.
• Yang X. S., 2010. Engineering Optimization: An Introduction with Metaheuristic Applications, John Wiley & Sons, XXVI.
• Zamani, M., Karimi-Ghartemani M., Sadati N. and Parniani M., 2009. Design of a fractional order PID controller for an AVR using particle swarm optimization. Control Engineering Practice, 17, 1380-7.
• Zhang Y., Wang S. and Ji G., 2015. A comprehensive survey on particle swarm optimization algorithm and its applications”, Mathematical Problems in Engineering, 2015, 1-38.