Araştırma Makalesi
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Metaheuristic Algorithms Based PID Controller Tuning Approach for Inverted Pendulum System

Yıl 2023, Cilt: 1 Sayı: 1, 37 - 50, 12.12.2023

Öz

Proportional, integral, derivative (PID) controllers, also known as proportional integral derivative controllers, are frequently used to regulate system outputs. PID parameter settings have a significant impact on system performance. There are various methods used to determine these parameters, and these methods have disadvantages. To overcome these drawbacks, metaheuristic optimization algorithms have been used. In this study, a linearized mathematical model of the inverted pendulum system was obtained. Controller parameters were obtained by applying the Ziegler-Nichols method to the linear model of the system. Then, the PID gain parameters of the inverted pendulum system were tuned by three different metaheuristic optimization methods which are particle swarm optimization (PSO), sine cosine optimization (SCA), and gray wolf optimization (GWO). It has been observed that the performance of the PID controller has increased significantly because of adjusting the control parameters with metaheuristic optimization algorithms. In this study, the results obtained from the integrated absolute error (IAE) fitness function as a result of the application of PSO, SCA and GWO methods were compared. Convergence graphs of PSO, SCA and GWO algorithms were obtained, and the convergence speed of the GWO algorithm was faster than the other two methods applied.

Kaynakça

  • [1] A. Bayram, A. S. Duru, (2022). Dynamics analysis of a head-neck rehabilitation robot using Newton-Euler equations. International Conference on Engineering Technologies (ICENTE 2022), Konya, Turkey, pp. 277-281.
  • [2] J. G. Ziegler, and N. B. Nichols, (1993). Optimum settings for automatic controllers. ASME Journal of Dynamic Systems, Measurement, and Control. 115, 220–222.
  • [3] R. Chotikunnan, P. Chotikunnan, A. Ma'arif, N. Thongpance, Y. Pititheeraphab, A. Srisiriwat, (2023). Ball and beam control: evaluating type-1 and interval type-2 fuzzy techniques with root locus optimization, International Journal of Robotics and Control Systems. 3,286-303.
  • [4] Ö. Gündoğdu, (2005). Optimal tuning of PID controller gains using genetic algorithms. Journal of Engineering Sciences, 11(1), 131-135.
  • [5] A. Jayachitra, and R. Vinodha, (2014). Genetic algorithm based PID controller tuning approach for continuous stirred tank reactor. Advances in Artificial Intelligence, 2014,1-8.
  • [6] D. P. Mishra, U. Raut, A. P. Gaur, S. Swain, S. Chauhan, (2023). Particle swarm optimization and genetic algorithms for PID controller tuning. Proceedings of the 5th International Conference on Smart Systems and Inventive Technology (ICSSIT 2023), Tirunelveli, India, 2023, pp. 189-194.
  • [7] H. Du, P. Liu, Q. Cui, X. Ma, H. Wang, (2022). PID controller parameter optimized by reformative artificial bee colony algorithm. Journal of Mathematics. 2022, 1-16.
  • [8] Y. T. Hsiao, C. L. Chuang, and C. C. Chien, (2004). Ant colony optimization for designing of PID controllers. International Conference on Robotics and Automation (IEEE 2004), New Orleans, LA, 2004, pp. 321-326.
  • [9] X. Z. Li, F. Yu, and Y. B. Wang, (2007). PSO algorithm based online self-tuning of PID controller. International Conference on Computational Intelligence and Security (CIS 2007), Harbin, China, 2007, pp. 128-132.
  • [10] F. Loucif, S. Kechida, A. Sebbagh (2020). Whale optimizer algorithm to tune PID controller for the trajectory tracking control of robot manipulator. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 42, 1-11.
  • [11] R. Paradhan, S. K. Majhi, J. K. Pradhan, B. B. Pati, (2018). Antlion optimizer tuned PID controller based on bode ideal transfer function for automobile cruise control system. Journal of Industrial Information Integration. 9, 45-52.
  • [12] B. Hekimoğlu, (2019). Sine-cosine algorithm-based optimization for automatic voltage regulator system. Transactions of the Institute of Measurement and Control. 41, 1761-1771.
  • [13] J. Mercieca, and S. G. Fabri, (2012). A metaheuristic particle swarm optimization approach to non-linear model predictive control. International Journal on Advances in Intelligent Systems. 5, 357-369.
  • [14] D. T. M. Phuong, P. V. Hung, N. N. Khoat, P. V. Minh, (2022). Balancing a practical inverted pendulum model employing novel metaheuristic optimization based fuzzy logic controller. International Journal of Advanced Computer Science and Applications. 13, 547-553.
  • [15] N. K. Nguyen, V. N. Pham, T. C. Ho, T. M. P. Dao, (2022). Designing an effective hybrid control strategy to balance a practical inverted pendulum system. International Journal of Engineering Trends and Technology. 70, 80-87.
  • [16] H. Wang, H. Zhou, D. Wang, S. Wen, (2013). Optimization of LQR controller for inverted pendulum system with artificial bee colony algorithm. International Conference on Advanced Mechatronic Systems, (IEEE 2013), Luoyang, China, pp. 158-162.
  • [17] A. Mourad, Y. Zennir, C. Tolba, (2022). Intelligent and robust controller tuned with WOA: applied for inverted pendulum. Journal Européen des Systèmes Automatisés. 55, 359-366
  • [18] J. Kennedy and R. Eberhart, (1995). Particle swarm optimization. International Conference on Neural Networks, Perth, WA, Australia, 1995, pp. 1942-1948.
  • [19] S. Mirjalili, (2016). A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems. 96, 120-133.
  • [20] S. Mirjalili, S. M. Mirjalili, A. Lewis, (2014). Grey wolf optimizer. Advances in Engineering Software. 69, 46-61.

Ters Sarkaç Sistemi için Meta Sezgisel Algoritmalara Dayalı PID Denetleyici Ayarlama Yaklaşımı

Yıl 2023, Cilt: 1 Sayı: 1, 37 - 50, 12.12.2023

Öz

Oransal integral türev kontrolörleri olarak da bilinen PID kontrolörleri, sistem çıkışlarını düzenlemek için sıklıkla kullanılır. PID parametre ayarlarının sistem performansı üzerinde önemli bir etkisi vardır. Bu parametrelerin belirlenmesinde kullanılan çeşitli yöntemler mevcut olup bu yöntemlerin dezavantajları bulunmaktadır. Bu olumsuzlukların üstesinden gelmek için meta sezgisel optimizasyon algoritmaları kullanılmıştır. Bu çalışmada ters sarkaç sisteminin doğrusallaştırılmış matematik modeli elde edilmiştir. Sistemin doğrusal modeline Ziegler-Nichols metodu uygulanarak kontrolcü parametreleri elde edilmiştir. Daha sonra ters sarkaç sisteminin PID kazanç parametreleri, parçacık sürüsü optimizasyonu (PSO), sinüs kosinüs optimizasyonu (SCA) ve gri kurt optimizasyonu (GWO) olmak üzere üç farklı meta sezgisel optimizasyon yöntemiyle ayarlanmıştır. Meta sezgisel optimizasyon algoritmalarıyla kontrol parametrelerinin ayarlanması sonucu PID kontrolcünün performansında önemli ölçüde artış meydana geldiği görülmüştür. Bu çalışmada PSO, SCA ve GWO yöntemlerinin uygulanması sonucu mutlak hata integrali uygunluk fonksiyonundan elde edilen sonuçlar karşılaştırılmıştır. PSO, SCA ve GWO metotlarının yakınsama grafikleri elde edilmiş olup GWO algoritmasının yakınsama hızı uygulanan diğer iki yönteme göre daha hızlı sonuç vermiştir.

Kaynakça

  • [1] A. Bayram, A. S. Duru, (2022). Dynamics analysis of a head-neck rehabilitation robot using Newton-Euler equations. International Conference on Engineering Technologies (ICENTE 2022), Konya, Turkey, pp. 277-281.
  • [2] J. G. Ziegler, and N. B. Nichols, (1993). Optimum settings for automatic controllers. ASME Journal of Dynamic Systems, Measurement, and Control. 115, 220–222.
  • [3] R. Chotikunnan, P. Chotikunnan, A. Ma'arif, N. Thongpance, Y. Pititheeraphab, A. Srisiriwat, (2023). Ball and beam control: evaluating type-1 and interval type-2 fuzzy techniques with root locus optimization, International Journal of Robotics and Control Systems. 3,286-303.
  • [4] Ö. Gündoğdu, (2005). Optimal tuning of PID controller gains using genetic algorithms. Journal of Engineering Sciences, 11(1), 131-135.
  • [5] A. Jayachitra, and R. Vinodha, (2014). Genetic algorithm based PID controller tuning approach for continuous stirred tank reactor. Advances in Artificial Intelligence, 2014,1-8.
  • [6] D. P. Mishra, U. Raut, A. P. Gaur, S. Swain, S. Chauhan, (2023). Particle swarm optimization and genetic algorithms for PID controller tuning. Proceedings of the 5th International Conference on Smart Systems and Inventive Technology (ICSSIT 2023), Tirunelveli, India, 2023, pp. 189-194.
  • [7] H. Du, P. Liu, Q. Cui, X. Ma, H. Wang, (2022). PID controller parameter optimized by reformative artificial bee colony algorithm. Journal of Mathematics. 2022, 1-16.
  • [8] Y. T. Hsiao, C. L. Chuang, and C. C. Chien, (2004). Ant colony optimization for designing of PID controllers. International Conference on Robotics and Automation (IEEE 2004), New Orleans, LA, 2004, pp. 321-326.
  • [9] X. Z. Li, F. Yu, and Y. B. Wang, (2007). PSO algorithm based online self-tuning of PID controller. International Conference on Computational Intelligence and Security (CIS 2007), Harbin, China, 2007, pp. 128-132.
  • [10] F. Loucif, S. Kechida, A. Sebbagh (2020). Whale optimizer algorithm to tune PID controller for the trajectory tracking control of robot manipulator. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 42, 1-11.
  • [11] R. Paradhan, S. K. Majhi, J. K. Pradhan, B. B. Pati, (2018). Antlion optimizer tuned PID controller based on bode ideal transfer function for automobile cruise control system. Journal of Industrial Information Integration. 9, 45-52.
  • [12] B. Hekimoğlu, (2019). Sine-cosine algorithm-based optimization for automatic voltage regulator system. Transactions of the Institute of Measurement and Control. 41, 1761-1771.
  • [13] J. Mercieca, and S. G. Fabri, (2012). A metaheuristic particle swarm optimization approach to non-linear model predictive control. International Journal on Advances in Intelligent Systems. 5, 357-369.
  • [14] D. T. M. Phuong, P. V. Hung, N. N. Khoat, P. V. Minh, (2022). Balancing a practical inverted pendulum model employing novel metaheuristic optimization based fuzzy logic controller. International Journal of Advanced Computer Science and Applications. 13, 547-553.
  • [15] N. K. Nguyen, V. N. Pham, T. C. Ho, T. M. P. Dao, (2022). Designing an effective hybrid control strategy to balance a practical inverted pendulum system. International Journal of Engineering Trends and Technology. 70, 80-87.
  • [16] H. Wang, H. Zhou, D. Wang, S. Wen, (2013). Optimization of LQR controller for inverted pendulum system with artificial bee colony algorithm. International Conference on Advanced Mechatronic Systems, (IEEE 2013), Luoyang, China, pp. 158-162.
  • [17] A. Mourad, Y. Zennir, C. Tolba, (2022). Intelligent and robust controller tuned with WOA: applied for inverted pendulum. Journal Européen des Systèmes Automatisés. 55, 359-366
  • [18] J. Kennedy and R. Eberhart, (1995). Particle swarm optimization. International Conference on Neural Networks, Perth, WA, Australia, 1995, pp. 1942-1948.
  • [19] S. Mirjalili, (2016). A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems. 96, 120-133.
  • [20] S. Mirjalili, S. M. Mirjalili, A. Lewis, (2014). Grey wolf optimizer. Advances in Engineering Software. 69, 46-61.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Kontrol Mühendisliği, Mekatronik ve Robotik (Diğer), Makine Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Ahmet Sadık Duru 0000-0001-9142-9344

Yayımlanma Tarihi 12 Aralık 2023
Gönderilme Tarihi 18 Ekim 2023
Kabul Tarihi 4 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 1 Sayı: 1

Kaynak Göster

APA Duru, A. S. (2023). Metaheuristic Algorithms Based PID Controller Tuning Approach for Inverted Pendulum System. Van Yüzüncü Yıl Üniversitesi Mühendislik Fakültesi Dergisi, 1(1), 37-50.