Application of optimal artificial intelligence based tuned controllers to a class of embedded nonlinear power system

Magdy A. S. Aboelela


This paper studies the implementation of the Bat Inspired Algorithm (BIA) as an optimization technique to find the optimal parameters of two classes of controllers. The first is the classical Proportional-Integral-Derivative (PID). The second is the hybrid fractional order and Brain Emotional Intelligent controller. The two controllers have been implemented, separately, for the load frequency control of a single area electric power system with three physical imbedded nonlinearities. The first nonlinearity represents the generation’s rate constraint (GRC). The second is owing to the governor dead band (GDB). The last is due to the time delay imposed by the governor-turbine link, the thermodynamic process, and the communication channels. These nonlinearities have been embedded in the simulation model of the system under study. Matlab/Simulink software has been applied to obtain the results of applying the two classes of controllers which have been, optimally, tuned using the BIA. The Integral of Square Error (ISE) criterion has been selected as an element of the objective function along with the percentage overshoot and settling time for the optimum tuning technique of the two controllers. The simulation results show that when using the hybrid fractional order and Brain Emotional Intelligent controller, it gives better response and performance indices than the conventional Proportional-Integral-Derivative (PID) controllers.


Brain emotional learning based intelligent controllers; Fraction Order Proportional- Integral-Derivative controllers; Matlab/Simulink; Nonlinear Systems; Overshoot; Proportional-Integral-Derivative controllers; Rise time

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