Adaptive Optimal Digital PID Controller

Abstract:

It is necessary to change the parameters of PID controller if the parameters of plants change or there are disturbances. Particle swarm optimization algorithm is a powerful optimization algorithm to find the global optimal values in the problem space. In this paper, the particle swarm optimization algorithm is used to identify the model of the plant and the parameter of digital PID controller online. The model of the plant is identified online according to the absolute error of the real system output and the identified model output. The digital PID parameters are tuned based on the identified model and they are adaptive if the model is changed. Simulations are done to validate the proposed method comparing with the classical PID controller.

• Periodical: Applied Mechanics and Materials (Volumes 789-790)
• Pages: 1021-1026
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.789-790.1021
• Online since: September 2015
• Authors: Yan Xia Sun*, Zeng Hui Wang
• Keywords: Adaptive Control, Parameter Tuning, Particle Swarm Optimization, PID Controller

References

[1] H. N. Koivo, Book review: Practical PID control-Antonio Visioli, IEEE Transactions on Automatic Control, vol. 53, pp.2217-2218, (2008).

DOI: 10.1109/tac.2008.920854

[2] M. Sam Fadali, Ditital Control Engineering: Analysis and design, Acadamic Press, (2009).

[3] J. G. Zigeler, Nichols N B. optimization setting for automatic controller, Trans. ASME, Vol. 64, No. 11, pp.756-769, (1942).

[4] G. H. Cohen and G. A. Coon, Theoretical Consideration of Retarded Control, Trans. ASME, vol. 75, pp.827-834, (1953).

DOI: 10.1115/1.4015452

[5] H. Han, and A. Luo, Nonlinear PID controller based on genetic tuning algorithm, Control and Decision, Vol. 20, No. 4, pp.448-450, (2005).

[6] J. S. Yang, PID control for a binary distillation column using a genetic searching algorithm, WSEAS Trans. Systems, Vol. 5, No. 4, p.720726, (2006).

[7] T. H. Kim, Maruta, and T. Sugie, Robust PID controller tuning based on the constrained particle swarm optimization, Automatica, vol. 44, Issue 4, pp.1104-1110, April (2008).

DOI: 10.1016/j.automatica.2007.08.017

[8] Y. Luo, J. Zhang, and X. Li, The optimization of PID controller parameters based on artificial fish swarm algorithm, in: proc. the IEEE International Conf. Automation and Logistics, Jinan, China, pp.1058-1062, August (2007).

DOI: 10.1109/ical.2007.4338724

[9] D. B. Fogel, Evolutionary computation: toward a new philosophy of a machine intelligence, IEEE Press, New York, (2000).

[10] A. Jalilvand, A. Kimiyahgahalam, A. Ashouri, M. Mahadavi, Advanced Particle Swarm Optimization-Based PID Controller Parameters Tuning, 12th IEEE International Multitopic Conference, Karachi , pp.429-435, Dec. (2008).

DOI: 10.1109/inmic.2008.4777776

[11] J. Kennedy, R. C. Eberhart, Particle Swarm Optimisation, Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp.1942-1948. (1995).

[12] X. Hu, Y. Shi, R. Eberhart, Recent Advances in Particle Swarm, Congress on Evolutionary Computation, pp.90-97. Piscataway, IEEE Service Center, (2004).

DOI: 10.1109/cec.2004.1330842

[13] C. M Huang, C. J. Huang, M. L. Wang, A Particle Swarm Optimisation to Identifying the ARMAX Model for Short term Load Forecasting, IEEE Transactions on Power Systems. 20, pp.1126-1133, (2005).

DOI: 10.1109/tpwrs.2005.846106

[14] M. Clerc, Particle Swarm Optimisation. ISTE Publishing Company, (2006).

[15] N. Nedjah, L. D. M. Mourelle, Systems Engineering Using Particle Swarm Optimisation, Nova Science Publishers, (2007).