Evolutionary Tuning of Optimal Controllers for Complex Systems

The Proportional Integral Derivative controller is the most widely used industrial device for monitoring and controlling processes. Although there are alternatives to the traditional rules of tuning, there is not yet a study showing that the use of heuristic algorithms it is indeed better than using the classic methods of optimal tuning. Current trends in controller parameter estimation minimize an integral performance criterion. In this paper, an evolutionary algorithm (MAGO - Multidynamics Algorithm for Global Optimization) is used as a tool to optimize the controller parameters minimizing the ITAE (Integral of Time multiplied by Absolut Error) performance index. The procedure is applied to a set of standard plants modelled as a Second Order System Plus Time Delay (SOSPD). Operating on servo and regulator modes and regardless the plant used, the evolutionary approach gets a better overall performance comparing to traditional methods (Bohl and McAvoy, Minimum ITAE-Hassan, Minimum ITAE-Sung). The solutions obtained cover all restrictions and extends the maximum and minimum boundaries between them.

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