A New Adaptive Particle Swarm Optimization Based on Self-Tuning of PID Controller for DC Motor System
Abstract
This paper presents a new adaptive particle swarm optimization algorithm for optimal self-tuning of PID controller in dc motor system. Manual tuning of PID controllers does not provide good performance, time consuming, difficult and tedious. The tuning process of PID controller is done by PSO algorithm. Inertia weight is the most important parameter in PSO algorithm, which gives a control of the exploration-exploitation characteristics of PSO algorithm. Since the beginning of Inertia Weight in PSO algorithm, Different strategies of PSO algorithm have been proposed in order to determine the inertia weight. In this paper, we propose a completely new strategy to adapt the inertia weight based on the fitness value of the particles. Comparing with standard PSO algorithm and time varying inertia weight PSO algorithm, the proposed adaptive PSO algorithm gives better performance in terms of quick convergence capability and continues movement toward the optimal solution region.
References
- 1. Berber, Ö., Ateş, M., Alhassan, H.A., Güneş, M., 2016. Parçacık Sürü Optimizasyonu ve PID ile Mobil Robotun Optimum Yörünge Kontrolü, Kahramanmaras Sutcu Imam University Journal of Engineering Sciences, 19(3), 165-169.
- 2. Çoban, R., Erçin, Ö., 2012. Multi-objective Bees Algorithm to Optimal Tuning of PID Controller, Cukurova University Journal of the Faculty of Engineering and Architecture, 27(2), 13-26.
- 3. Chang, W.D., Hwang, R.C., Hsieh, J.G., 2003. A Multivariable On-line Adaptive PID Controller Using Auto-tuning Neurons, Engineering Applications of Artificial Intelligence, 16(1), 57-63.
- 4. Gündoğdu, Ö., 2005. Optimal-tuning of PID Controller Gains Using Genetic Algorithms, Journal of Engineering Sciences, 11(1), 131-135.
- 5. Hassani, K., Lee, W.S., 2014. May. Optimal Tuning of Linear Quadratic Regulators Using Quantum Particle Swarm Optimization, In Proceedings of the International Conference on Control, Dynamic Systems, and Robotics (CDSR’14) 14-15.
- 6. Çoban, R., 2011. A Fuzzy Controller Design for Nuclear Research Reactors using the Particle Swarm Optimization Algorithm, Nuclear Engineering and Design, 241(5), 1899-1908.
- 7. Çoban, R., 2014. Power Level Control of the TRIGA Mark-II Research Reactor Using the Multifeedback Layer Neural Network and the Particle Swarm Optimization, Annals of Nuclear Energy, 69, 260-266.
- 8. Bansal, J.C., Singh, P.K., Saraswat, M., Verma, A., Jadon, S.S., Abraham, A., 2011. October. Inertia Weight Strategies in Particle Swarm Optimization, In Nature and Biologically Inspired Computing (NaBIC), 633-640.
- 9. Shi, Y., 2001. Particle Swarm Optimization: Developments, Applications and Resources. In Evolutionary Computation, vol. 1, 81-86.
- 10. Shi, Y., Eberhart, R., 1998. A Modified Particle Swarm Optimizer, In Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, 69-73.
- 11. Shi, Y., Eberhart, R.C., 1999. Empirical Study of Particle Swarm Optimization. In Evolutionary Computation, vol. 3, 1945-1950.
- 12. Meshram, P.M., Kanojiya, R.G., 2012. Tuning of PID Controller Using Ziegler-Nichols Method for Speed Control of DC Motor, In Advances in Engineering, Science and Management (ICAESM), 117-122.
- 13. Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization, Proceedings of the 4th IEEE International Conference on Neural Networks, 1942-1948.
- 14. Eberhart, R., Kennedy, J., 1995. A New Optimizer Using Particle Swarm Theory, In Micro Machine and Human Science, MHS’95, 39-43.
- 15. Eberhart, R.C., Shi, Y., 2001. Tracking and Optimizing Dynamic Systems With Particle Swarms, In Evolutionary Computation, vol. 1, 94-100.
- 16. Xin, J., Chen, G., Hai, Y., 2009. A Particle Swarm Optimizer With Multi-stage Linearly-decreasing Inertia Weight, In Computational Sciences and Optimization, 2009. vol. 1, 505-508.
Yeni Bir Adaptif Parçacık Sürü Optimizasyon Algoritması Kullanarak DC Motor için Öz Ayarlamalı PID Kontrolör Tasarımı
Abstract
Bu çalışmada, DC motor sisteminde yeni bir adaptif parçacık sürü optimizasyon algoritması kullanarak öz ayarlamalı PID kontrolör tasarlanmıştır. PID kontrolörleri parametre ayarlarının manuel yapılması zaman alıcı olması, uzun sürmesi ve hassas olmaması gibi nedenlerden dolayı her zaman iyi bir performans sağlamaz. Bu çalışmada PID kontrolörün parametre ayarları, Parçacık Sürü Optimizasyonu (PSO) algoritması ile yapılmıştır. Eylemsizlik ağırlığı PSO algoritmasında en önemli parametredir. Eylemsizlik ağırlığı, PSO algoritmasının arama özelliğini kontrol eder. PSO algoritmasının başlangıcından bu yana, uygun eylemsizlik ağırlığı belirlemek için farklı PSO algoritması stratejileri önerilmiştir. Bu çalışmada, parçacıkların uygunluk değerlerinin karşılaştırılmasına dayanılarak eylemsizlik ağırlığı ayarlamak için yeni bir strateji önerilmiştir. Standart PSO algoritması (S-PSO) ve zaman değişen eylemsizlik ağırlığı PSO algoritması (TVIW-PSO) ile karşılaştırıldığında, önerilen adaptif PSO algoritması, hızlı yakınsama ve optimal çözüme doğru harekete devam etmesi açısından daha iyi performans verdiği gözlenmiştir.
References
- 1. Berber, Ö., Ateş, M., Alhassan, H.A., Güneş, M., 2016. Parçacık Sürü Optimizasyonu ve PID ile Mobil Robotun Optimum Yörünge Kontrolü, Kahramanmaras Sutcu Imam University Journal of Engineering Sciences, 19(3), 165-169.
- 2. Çoban, R., Erçin, Ö., 2012. Multi-objective Bees Algorithm to Optimal Tuning of PID Controller, Cukurova University Journal of the Faculty of Engineering and Architecture, 27(2), 13-26.
- 3. Chang, W.D., Hwang, R.C., Hsieh, J.G., 2003. A Multivariable On-line Adaptive PID Controller Using Auto-tuning Neurons, Engineering Applications of Artificial Intelligence, 16(1), 57-63.
- 4. Gündoğdu, Ö., 2005. Optimal-tuning of PID Controller Gains Using Genetic Algorithms, Journal of Engineering Sciences, 11(1), 131-135.
- 5. Hassani, K., Lee, W.S., 2014. May. Optimal Tuning of Linear Quadratic Regulators Using Quantum Particle Swarm Optimization, In Proceedings of the International Conference on Control, Dynamic Systems, and Robotics (CDSR’14) 14-15.
- 6. Çoban, R., 2011. A Fuzzy Controller Design for Nuclear Research Reactors using the Particle Swarm Optimization Algorithm, Nuclear Engineering and Design, 241(5), 1899-1908.
- 7. Çoban, R., 2014. Power Level Control of the TRIGA Mark-II Research Reactor Using the Multifeedback Layer Neural Network and the Particle Swarm Optimization, Annals of Nuclear Energy, 69, 260-266.
- 8. Bansal, J.C., Singh, P.K., Saraswat, M., Verma, A., Jadon, S.S., Abraham, A., 2011. October. Inertia Weight Strategies in Particle Swarm Optimization, In Nature and Biologically Inspired Computing (NaBIC), 633-640.
- 9. Shi, Y., 2001. Particle Swarm Optimization: Developments, Applications and Resources. In Evolutionary Computation, vol. 1, 81-86.
- 10. Shi, Y., Eberhart, R., 1998. A Modified Particle Swarm Optimizer, In Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, 69-73.
- 11. Shi, Y., Eberhart, R.C., 1999. Empirical Study of Particle Swarm Optimization. In Evolutionary Computation, vol. 3, 1945-1950.
- 12. Meshram, P.M., Kanojiya, R.G., 2012. Tuning of PID Controller Using Ziegler-Nichols Method for Speed Control of DC Motor, In Advances in Engineering, Science and Management (ICAESM), 117-122.
- 13. Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization, Proceedings of the 4th IEEE International Conference on Neural Networks, 1942-1948.
- 14. Eberhart, R., Kennedy, J., 1995. A New Optimizer Using Particle Swarm Theory, In Micro Machine and Human Science, MHS’95, 39-43.
- 15. Eberhart, R.C., Shi, Y., 2001. Tracking and Optimizing Dynamic Systems With Particle Swarms, In Evolutionary Computation, vol. 1, 94-100.
- 16. Xin, J., Chen, G., Hai, Y., 2009. A Particle Swarm Optimizer With Multi-stage Linearly-decreasing Inertia Weight, In Computational Sciences and Optimization, 2009. vol. 1, 505-508.
Details
| Journal Section | Articles |
|---|---|
| Authors | |
| Publication Date | September 15, 2017 |
| Published in Issue | Year 2017 Volume: 32 Issue: 3 |