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Optimizing PID Tuning Parameters Using Grey Prediction Algorithm
Ala Eldin Abdallah Awouda, Rosbi Bin Mamat
Pages - 26 - 36     |    Revised - 25-02-2010     |    Published - 26-03-2010
Volume - 4   Issue - 1    |    Publication Date - March 2010  Table of Contents
PID, Grey prediction algorithm, AMIGO, ITAE index
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.
CITED BY (1)  
1 Wang, B., Xu, S. H., Meng, Y. H., & Wang, X. L. (2013). A process neural network based on improved particle swarm optimization and its application in PID control. Advances in Information Sciences and Service Sciences, 5(7), 701.
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Mr. Ala Eldin Abdallah Awouda
UTM - Malaysia
Professor Rosbi Bin Mamat
UTM - Malaysia