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Optimizing PID Tuning Parameters Using Grey Prediction Algorithm
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International Journal of Engineering (IJE)
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Volume:  4    Issue:  1
Pages:  1-104
Publication Date:   March 2010
ISSN (Online): 1985-2312
Pages 
26 - 36
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Published Date   
26-03-2010 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
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Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   PID, Grey prediction algorithm, AMIGO, ITAE index 
 
 
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This paper discusses an approach to tune the PID controller parameters using the optimization method and grey prediction algorithm. The method involves calculating the average of the estimated error using grey prediction algorithm. A mat lab program is developed using simulink to find the average of the estimated error for the system whose process is modeled in first order lag plus dead time (FOLPD) form. the Optimization method with mat lab software program was used to find the optimum value for the controller gain (Kc (opt)) which minimizes specific performance criteria (ITAE performance criteria) to achieve most of the systems requirements such as reducing the overshoot, maintain a high system response, achieve a good load disturbances rejection and maintain robustness. The average of the estimated error had been calculated using grey prediction algorithm. Those two parameters were used to calculate the gain of the controller (Kc), integral time (Ti) and the derivative time (Td) for PID controller. Simulations for the proposed algorithm had been done for different process models. A comparison between the proposed tuning rules and the traditional tuning rules is done through the Matlab software to show the efficiency of the new tuning rule. 
 
 
 
1 Dingyu Xue, YangQuan Chen. Derek P. Atherton. "Linear Feedback Control" Society for Industrial and Applied Mathematics. 2007.
2 Guillermo J. Silva, Aniruddha Datta. S. P. Bhattacharyya. “PID Controllers for Time-Delay Systems”. Boston. ISBN 0-8176-4266-8.2005.
3 Hang, C.C, K.J.Astrom and W.K. Ho, “refinement of Ziegler-Nichols Tuning Formula”, IEE Proc. Pt. D, Vol. 138, pp. 111-118 .1991.
4 J. G. Ziegler and N. B. Nichols, “Optimum Settings for Automatic Controllers,” Trans. ASME, Vol. 64, pp. 759-768, 1942.
5 J. L. Deng, "Introduction to Grey System Theory," The Journal of Grey System, vol., pp. 1- 241, 1989.
6 K.J.Astrom, T.Hagglund, “Revisiting the Ziegler–Nichols step response method for PID control”, Journal of Process Control 14,635–650, Department of Automatic Control, Lund Institute of Technology. 2004.
7 Lasse M. Eriksson and Mikael Johansson, “PID Controller Tuning Rules for Varying Time- Delay Systems”, Proceedings of the 2007 American Control Conference ,New York City, USA, July 11-13, 2007.
8 Panagopoulos H., Astrom K. J., Hagglund T., “Design of PID Controllers Based on Constrained Optimisation”, IEE Proc., Control Theory Appl., Vol. 149, No. 1. – P. 32–40. 2002.
9 P. Cominos and N. Munro, “PID Controllers: Recent Tuning Methods and Design to Specification,” IEE Proc. D, Control Theory and Applications, Vol. 149, No. 1, pp. 46-53, 2002.
10 Si-Feng Liu, Member, IEEE, Jeffrey Forrest, “Advances in Grey Systems Theory and Its Applications”, Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Services, November 18-20, 2007, Nanjing, China.
 
 
 
 
 
 
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Ala Eldin Abdallah Awouda : Colleagues
Rosbi Bin Mamat : Colleagues  
 
 
 
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