Araştırma Makalesi
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PID Kontrolörün Kazanç Katsayılarının Optimizasyonu için Farklı Yöntemlerin Karşılaştırılması

Yıl 2023, Cilt: 9 Sayı: 2, 254 - 264, 31.12.2023
https://doi.org/10.34186/klujes.1310728

Öz

Orantılı-İntegral-Türev (PID) denetleyici, sağlamlığı ve uygulama kolaylığı nedeniyle teknik uygulamalarda yaygın olarak kullanılmaktadır. Bir PID denetleyicinin kazanç değerleri, oturma zamanı, yükselme zamanı ve aşma gibi performans kriterleri üzerinde güçlü bir etkiye sahiptir. Bu kriterlerin en az değerine sahip sistemler, güçlü kontrol sistemleri olarak kabul edilir. Kapalı döngü kontrol sistemlerinin, en iyi basamak tepkisini elde etmek için parametrelerin ayarlanması karmaşık bir işlemdir. Parametre değerlerini hesaplamak için başlangıçta Ziegler-Nichols (ZN) yöntemi gibi uzun zamandır bilinen yöntemler kullanılırken, günümüzde metasezgisel algoritmalar kullanılmaktadır. Bu makale, üçüncü dereceden transfer fonksiyonuna sahip bir sistemin kontrolü için metasezgisel algoritmalar kullanan bir PID kontrol cihazının kazanç parametrelerinin ayarlanmasına odaklanmaktadır. Önerilen algoritmalar, Bulanık Mantık (FL), Genetik Algoritma (GA) ve Parçacık Sürü Optimizasyonudur (PSO). Karşılaştırma sonuçları GA'nın optimizasyon için en iyi algoritma olduğu sonucuna varmıştır.

Kaynakça

  • Issa, M., Elbaset, A. A., Hassanien, A. E. and Ziedan, I., PID Controller Tuning Parameters Using Meta-heuristics Algorithms: Comparative Analysis, in Machine Learning Paradigms: Theory and Application, Studies in Computational Intelligence, vol. 801. Cham: Springer International Publishing, (2019), pp. 413–430. doi: 10.1007/978-3-030-02357-7_20.
  • Hamid, H.A., Md Mahanijah, K., Faieza, H.Y., Application of PID controller in controlling refrigerator temperature, In: 5th International Colloquium on Signal Processing & Its Applications CSPA 2009. IEEE (2009)
  • Bai, Y., Chen, R., Zhao, Y. and Wang, Y., Gaussian mixture model based adaptive control for uncertain nonlinear systems with complex state constraints, Chinese Journal of Aeronautics, vol. 35, no. 5, pp. 361–373, (2022), doi: 10.1016/j.cja.2021.06.017.
  • Li, Y. and Ma, D., Robust PID Control of Second-Order Uncertain Nonlinear System with Time-Varying Delay: An Input-Output Approach, IFAC-PapersOnLine, vol. 54, no. 18, pp. 70–75, (2021), doi: 10.1016/j.ifacol.2021.11.118.
  • Ma, D., Boussaada, I., Chen, J., Bonnet, C., Niculescu, S.I. and Chen, J., PID control design for first-order delay systems via MID pole placement: Performance vs. robustness, Automatica, vol. 137, p. 110102, (2022), doi: 10.1016/j.automatica.2021.110102.
  • Sam, S.M. and Angel, T.S., Performance optimization of PID controllers using fuzzy logic, in IEEE International Conference on Smart Technologies and Management for Computing, (2017) Communication, Controls, Energy and Materials (ICSTM), Chennai, India: IEEE, Aug. 2017, pp. 438–442. doi: 10.1109/ICSTM.2017.8089200.
  • Zhang, J. and Guo, L., PID Control of Nonlinear Stochastic Systems with Structural Uncertainties, IFAC-PapersOnLine, vol. 53, no. 2, pp. 2189–2194, (2020), doi: 10.1016/j.ifacol.2020.12.002.
  • Ziegler, J.G., Nichols, N.B., Optimum settings for automatic controllers, Trans. ASME 64(11) (1942)
  • Ou, C. and Lin, W., Comparison Between PSO and GA For Parameters Optimization of PID Controller, Proc. IEEE International Conference on Mechatronics And Automation, Louyang, China, (2006).
  • Shi, Y.H. and Eberhart, R.C., A modified particle swarm optimizer. IEEE Internationa Conference on Evolutionary Computation, Anchorage, Alaska. (1998).
  • Sridhar, R., et al., Optimization of heterogeneous bin packing using adaptive genetic algorithm., In: IOP Conference Series.Materials Science and Engineering, vol. 183. no. 1. IOP Publishing (2017)
  • Lai, C., et al., Genetic algorithm based current optimization for torque ripple reduction of interior PMSMs., IEEE Trans. Ind. Appl. (2017)
  • Saljoughi, E., Application of genetic programming as a powerful tool formodeling of cellulose acetate membrane preparation., Chem. Ind. 1, 4 (2017)
  • Barley, M.H., Turner, N.J., Goodacre, R., Recommendations on the implementation of genetic algorithms for the directed evolution of enzymes for industrial purposes., ChemBioChem (2017)
  • Ben, J. A., Coopération méta heuristique et logique floue pour le dimensionnement d'une installation hybride., Thèse pour obtenir le grade de docteur, Université de Reims Champagne-Ardenne, Reims, France (2015)

Comparison of Different Methods for Optimization of PID Controller Gain Coefficients

Yıl 2023, Cilt: 9 Sayı: 2, 254 - 264, 31.12.2023
https://doi.org/10.34186/klujes.1310728

Öz

Proportional-Integral-Derivative (PID) controller is widely used in technical applications due to its robustness and ease of application. The gain values of a PID controller have a strong impact on performance criteria such as settling time, rise time, and overshoot. Systems that possess at least one of these criteria are considered strong control systems. Adjusting the parameters to obtain the best step response of closed loop control systems is a complex operation. While long known methods such as the Ziegler-Nichols (ZN) method were initially used to compute parameter values, today, metaheuristic algorithms are employed. This article focuses on the tuning of gain parameters of a PID controller using metaheuristic algorithms for the control of a system with a third-order transfer function. The proposed algorithms are Fuzzy Logic (FL), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The comparison results concluded that GA is the best algorithm for optimization.

Kaynakça

  • Issa, M., Elbaset, A. A., Hassanien, A. E. and Ziedan, I., PID Controller Tuning Parameters Using Meta-heuristics Algorithms: Comparative Analysis, in Machine Learning Paradigms: Theory and Application, Studies in Computational Intelligence, vol. 801. Cham: Springer International Publishing, (2019), pp. 413–430. doi: 10.1007/978-3-030-02357-7_20.
  • Hamid, H.A., Md Mahanijah, K., Faieza, H.Y., Application of PID controller in controlling refrigerator temperature, In: 5th International Colloquium on Signal Processing & Its Applications CSPA 2009. IEEE (2009)
  • Bai, Y., Chen, R., Zhao, Y. and Wang, Y., Gaussian mixture model based adaptive control for uncertain nonlinear systems with complex state constraints, Chinese Journal of Aeronautics, vol. 35, no. 5, pp. 361–373, (2022), doi: 10.1016/j.cja.2021.06.017.
  • Li, Y. and Ma, D., Robust PID Control of Second-Order Uncertain Nonlinear System with Time-Varying Delay: An Input-Output Approach, IFAC-PapersOnLine, vol. 54, no. 18, pp. 70–75, (2021), doi: 10.1016/j.ifacol.2021.11.118.
  • Ma, D., Boussaada, I., Chen, J., Bonnet, C., Niculescu, S.I. and Chen, J., PID control design for first-order delay systems via MID pole placement: Performance vs. robustness, Automatica, vol. 137, p. 110102, (2022), doi: 10.1016/j.automatica.2021.110102.
  • Sam, S.M. and Angel, T.S., Performance optimization of PID controllers using fuzzy logic, in IEEE International Conference on Smart Technologies and Management for Computing, (2017) Communication, Controls, Energy and Materials (ICSTM), Chennai, India: IEEE, Aug. 2017, pp. 438–442. doi: 10.1109/ICSTM.2017.8089200.
  • Zhang, J. and Guo, L., PID Control of Nonlinear Stochastic Systems with Structural Uncertainties, IFAC-PapersOnLine, vol. 53, no. 2, pp. 2189–2194, (2020), doi: 10.1016/j.ifacol.2020.12.002.
  • Ziegler, J.G., Nichols, N.B., Optimum settings for automatic controllers, Trans. ASME 64(11) (1942)
  • Ou, C. and Lin, W., Comparison Between PSO and GA For Parameters Optimization of PID Controller, Proc. IEEE International Conference on Mechatronics And Automation, Louyang, China, (2006).
  • Shi, Y.H. and Eberhart, R.C., A modified particle swarm optimizer. IEEE Internationa Conference on Evolutionary Computation, Anchorage, Alaska. (1998).
  • Sridhar, R., et al., Optimization of heterogeneous bin packing using adaptive genetic algorithm., In: IOP Conference Series.Materials Science and Engineering, vol. 183. no. 1. IOP Publishing (2017)
  • Lai, C., et al., Genetic algorithm based current optimization for torque ripple reduction of interior PMSMs., IEEE Trans. Ind. Appl. (2017)
  • Saljoughi, E., Application of genetic programming as a powerful tool formodeling of cellulose acetate membrane preparation., Chem. Ind. 1, 4 (2017)
  • Barley, M.H., Turner, N.J., Goodacre, R., Recommendations on the implementation of genetic algorithms for the directed evolution of enzymes for industrial purposes., ChemBioChem (2017)
  • Ben, J. A., Coopération méta heuristique et logique floue pour le dimensionnement d'une installation hybride., Thèse pour obtenir le grade de docteur, Université de Reims Champagne-Ardenne, Reims, France (2015)
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Kontrol Mühendisliği, Mekatronik ve Robotik (Diğer)
Bölüm Sayı
Yazarlar

Gülten Yılmaz 0000-0002-7555-6658

Yayımlanma Tarihi 31 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 9 Sayı: 2

Kaynak Göster

APA Yılmaz, G. (2023). Comparison of Different Methods for Optimization of PID Controller Gain Coefficients. Kirklareli University Journal of Engineering and Science, 9(2), 254-264. https://doi.org/10.34186/klujes.1310728