The Control of A Non-Linear Chaotic System Using Genetic and Particle Swarm Based On Optimization Algorithms

Abstract

In this study, the control of a non-linear system was realized by using a linear system control strategy. According to the strategy and by using the controller coefficients, system outputs were controlled for all reference points with the same coefficients via focused references. In the framework of this study, the Lorenz chaotic system as non-linear structure, and the discrete-time PI algorithm as the control algorithm has selected. The genetic algorithm and particle swarm optimization methods have used in the optimization process, and the success of both methods has been discussed among themselves. Closed-loop control system has run simultaneously under the Matlab / Simulink programmer. The results have discussed by using the ISE, IAE, ITAE error criteria, and improved dTISDSE purpose functions.

Keywords

• Lorenz chaotic system,discrete-time PI controller,genetic algorithm (GA),particle swarm optimization (PSO).

Kaynakça

1. Puja Dash, Lalit Chandra Saikia, Nidul Sinha (2015). Automatic generation control of multi area thermal system using Bat algorithm optimized PD–PID cascade controller. Electrical Power and Energy Systems. Vol. 68. Pages. 364–372.
2. Marco Calvini, Mauro Carpita, Andrea Formentini (2015). PSO-Based Self-Commissioning of Electrical Motor Drives. IEEE Transactions on Industrial Electronics. Vol. 62. Pages. 768-776.
3. https://tr.wikipedia.org/wiki/Genetik_algoritma
4. James Kennedy, Russell Eberhart (1995). Particle swarm optimization. Proceedings of the Fourth IEEE International Conference on Neural Networks. Pages. 1942-1948.
5. http://www.swarmintelligence.org/tutorials.php
6. Nadia Nedjah, Rogério de M. Calazan, Luiza de Macedo Mourelle, Chao Wang (2016). Parallel Implementations of the Cooperative Particle Swarm Optimization on Many-core and Multi-core Architectures. Int J Parallel Prog. Vol. 44. Pages. 1173–1199.
7. M. Mehdinejad, B.M. Ivatloo, R.D. Bonab, K. Zare (2016). Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms. Electrical Power and Energy Systems. Vol. 83. Pages. 104–116.
8. Zhiqiang Geng, Zun Wang, Qunxiong Zhu, Yongming Han (2016). Multi-objective operation optimization of ethylene cracking furnace based on AMOPSO algorithm. Chemical Engineering Science. Vol. 153. Pages. 21–33.

9. Chijun Zhang, Yongjian Yang, Zhanwei Du, Chuang Ma (2016). Particle swarm optimization algorithm based on ontology model to support cloud computing applications. Journal of Ambient Intelligence and Humanized Computing. Vol. 7. Issue. 5. Pages. 633–638.
10. Q. Wu, F. Xiong, F. Wang & Y. Xiong (2016). Parallel particle swarm optimization on a graphics processing unit with application to trajectory optimization. Engineering Optimization. Vol. 48. Pages. 1679–1692.
11. V. Jeyalakshmi, P. Subburaj (2016). PSO-scaled fuzzy logic to load frequency control in hydrothermal power system. Soft Computing. Vol. 20. Pages. 2577–2594.
12. Nitin Kumar Saxena, Ashwani Kumar (2016). Reactive power control in decentralized hybrid power system with STATCOM using GA, ANN and ANFIS methods. International Journal of Electrical Power & Energy Systems. Vol. 83. Pages. 175–187.
13. A. Noshadi, J. Shi, Wee Sit Lee, P. Shi, A. Kalam (2016). Optimal PID-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system. Neural Computing and Applications. Vol. 27. Issue. 7. Pages. 2031–2046.
14. Pawel Olszewski (2016). Genetic optimization and experimental verification of complex parallel pumping station with centrifugal pumps. Applied Energy. Vol. 178. Pages. 527–539.
15. Aydın Mühürcü, Ercan Köse (2016). Optimal Control of Output Signal of a Chaotic Oscillator Using Genetic Algorithm Based Discrete Time PI Controller. International Journal of Innovative Research in Science, Engineering and Technology. Vol. 5. Special Issue 12. Pages 187-195.
16. R.C. Eberhart, J. Kennedy (1995). Particle swarm optimization, in: Proc. of IEEE Int. Conf. on Neural Network, Perth, Australia. Pages 1942–1948.