Comparison Performances of PSO and GA to Tuning PID Controller for the DC Motor

Year 2019, Volume: 23 Issue: 2, 162 - 174, 01.04.2019

Harun Yazgan , Furkan Yener , Semih Soysal Ahmet Gür

https://doi.org/10.16984/saufenbilder.376464

Cited By: 6

Abstract

A DC motor widely uses for sensitive
speed and position in industry. Stability and productivity of a system are
important for controlling of a DC motor speed. Stable of speed which affected
from load fluctuation and environmental factors. Therefore, it is important for
the speed value which is required as constant and to keep it as its value. In
this study, it is aimed that the speed value which is achieved as required
value and keeping it as constant using Proportional, Integral and Derivative
(PID) controller for tuning parameters. Firstly, Ziegler-Nichols (ZN) is one of
a traditional method used. PID parameters are determined with responses of
open-loop under running system. Later, parameters of the PID are estimated
using two metaheuristic algorithms such as Particle Swarm Optimization (PSO)
and Genetic Algorithm (GA). As a result, three algorithms’ results are compared
based on five criteria.  The PSO
algorithm produces better results than Genetic Algorithm for each criteria.

Keywords

DC motor, PID, PSO, GA

References

• R. G. Kanojiya and P. M. Meshram, “Optimal tuning of PI controller for speed control of DC motor drive using particle swarm optimization,” in Advances in Power Conversion and Energy Technologies (APCET), 2012 International Conference on, 2012, pp. 1–6.
• N. Thomas and D. P. Poongodi, “Position control of DC motor using genetic algorithm based PID controller,” in Proceedings of the World Congress on Engineering, 2009, vol. 2, pp. 1–3.
• J. C. Basilio and S. R. Matos, “Design of PI and PID controllers with transient performance specification,” IEEE Trans. Educ., vol. 45, no. 4, pp. 364–370, 2002.
• S. G. Kumar, R. Jain, N. Anantharaman, V. Dharmalingam, and K. Begum, “Genetic algorithm based PID controller tuning for a model bioreactor,” Indian Chem. Eng., vol. 50, no. 3, pp. 214–226, 2008.
• M. K. Tan, Y. K. Chin, H. J. Tham, and K. T. K. Teo, “Genetic algorithm based PID optimization in batch process control,” in Computer Applications and Industrial Electronics (ICCAIE), 2011 IEEE International Conference on, 2011, pp. 162–167.
• A. D. Lidbe, A. M. Hainen, and S. L. Jones, “Comparative study of simulated annealing, tabu search, and the genetic algorithm for calibration of the microsimulation model,” Simulation, vol. 93, no. 1, pp. 21–33, 2017.
• Z.-L. Gaing, “A particle swarm optimization approach for optimum design of PID controller in AVR system,” IEEE Trans. Energy Convers., vol. 19, no. 2, pp. 384–391, 2004.
• B. Allaoua, B. Gasbaoui, and B. Mebarki, “Setting up PID DC motor speed control alteration parameters using particle swarm optimization strategy,” Leonardo Electron. J. Pract. Technol., vol. 14, pp. 19–32, 2009.
• R. Dong, “Differential evolution versus particle swarm optimization for PID controller design,” in 2009 Fifth International Conference on Natural Computation, 2009, vol. 3, pp. 236–240.
• Y.-T. Hsiao, C.-L. Chuang, and C.-C. Chien, “Ant colony optimization for designing of PID controllers,” in Computer Aided Control Systems Design, 2004 IEEE International Symposium on, 2004, pp. 321–326.
• I. Chiha, N. Liouane, and P. Borne, “Tuning PID controller using multiobjective ant colony optimization,” Appl. Comput. Intell. Soft Comput., vol. 2012, p. 11, 2012.
• X. Dong and others, “The PID Controller Based on the Artificial Neural Network and the Differential Evolution Algorithm,” 2012.
• V. Rajinikanth and K. Latha, “I-PD controller tuning for unstable system using bacterial foraging algorithm: a study based on various error criterion,” Appl. Comput. Intell. Soft Comput., vol. 2012, p. 2, 2012.
• S. Duman, D. Maden, and U. Güvenç, “Determination of the PID controller parameters for speed and position control of DC motor using gravitational search algorithm,” in Electrical and Electronics Engineering (ELECO), 2011 7th International Conference on, 2011, p. I–225.
• R. E. Haber, R. Haber-Haber, R. M. Del Toro, and J. R. Alique, “Using Simulated Annealing for Optimal Tuning of a PID Controller for Time-Delay Systems. An Application to a High-Performance Drilling Process,” in International Work-Conference on Artificial Neural Networks, 2007, pp. 1155–1162.
• Y. Peng, X. Luo, and W. Wei, “A New Control Method Based on Artificial Immune Adaptive Strategy,” Elektron. Ir Elektrotechnika, vol. 19, no. 4, pp. 3–8, 2013.
• P. Varma and B. A. Kumar, “Control of DC motor using artificial bee colony based PID controller,” Int J Digit. Appl Contemp Res, vol. 2, pp. 1–9, 2013.
• H. John, Adaptation in natural and artificial systems. MIT Press, Cambridge, MA, 1992.
• R. Ebenhart, “Kennedy. Particle swarm optimization,” in Proceeding IEEE Inter Conference on Neural Networks, Perth, Australia, Piscat-away, 1995, vol. 4, pp. 1942–1948.
• A. K. Mishra, V. K. Tiwari, R. Kumar, and T. Verma, “Speed control of DC motor using artificial bee colony optimization technique,” in Control, Automation, Robotics and Embedded Systems (CARE), 2013 International Conference on, 2013, pp. 1–6.
• G. Pereira, “Particle Swarm Optimization,” INESCID Inst. Super. Techno Porto Salvo Port., 2011.
• D. E. Golberg, “Genetic algorithms in search, optimization, and machine learning,” Addion Wesley, vol. 1989, p. 102, 1989.
• D. H. Kim, W. P. Hong, and J. I. Park, “Auto-tuning of reference model based PID controller using immune algorithm,” in Evolutionary Computation, 2002. CEC’02. Proceedings of the 2002 Congress on, 2002, vol. 1, pp. 483–488.
• A. Schmidt, U. Durak, and T. Pawletta, “Model-based testing methodology using system entity structures for MATLAB/Simulink models,” Simulation, vol. 92, no. 8, pp. 729–746, 2016.
• S. P. Ghoshal, “Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control,” Electr. Power Syst. Res., vol. 72, no. 3, pp. 203–212, 2004.
• R. A. Krohling and J. P. Rey, “Design of optimal disturbance rejection PID controllers using genetic algorithms,” IEEE Trans. Evol. Comput., vol. 5, no. 1, pp. 78–82, 2001.
• Y. Mitsukura, T. Yamamoto, and M. Kaneda, “A design of self-tuning PID controllers using a genetic algorithm,” in American Control Conference, 1999. Proceedings of the 1999, 1999, vol. 2, pp. 1361–1365.
• J. Zhang, J. Zhuang, H. Du, and others, “Self-organizing genetic algorithm based tuning of PID controllers,” Inf. Sci., vol. 179, no. 7, pp. 1007–1018, 2009.
• L. Fan and E. M. Joo, “Design for auto-tuning PID controller based on genetic algorithms,” in 2009 4th IEEE Conference on Industrial Electronics and Applications, 2009, pp. 1924–1928.
• K.-E. \AArzén, “A simple event-based PID controller,” in 14th IFAC world congress, 1999.
• A. A. El-Gammal and A. A. El-Samahy, “Adaptive tuning of a PID speed controller for DC motor drives using multi-objective particle swarm optimization,” in Computer Modelling and Simulation, 2009. UKSIM’09. 11th International Conference on, 2009, pp. 398–404.