Pole Placement Using Genetic Algorithm with Integral Control

This paper presents the application of genetic algorithms to find the optimum feedback gains in pole placement with integral control to get the desired performance in control system. Performances are measured in terms of key parameters like settling time, peak overshoot, undershoot, rise time etc in control system. The major drawbacks of pole placement technique are to get optimum location of poles and we cannot place zeros at desired locations. Dominating concept of poles is enough for placing poles for systems which transfer functions are without zeros. But if there are zeros in transfer function then it is not enough to place pole with dominating pole concept because presence of zero can destroy the dynamic response of control system. In this paper genetic algorithm is used to find the optimum locations of poles to get the desired response and compensate the effect of zeros on transient in case zeros are present in transfer function. GA is a population based algorithm to find the global solution of a problem. This paper compares the unit step response of a plant with presence of zero in transfer function. The same GA based technique is used for finding optimum location of poles to get the desired response for magnetic levitation system. This analysis is well supported by the simulation and experimental results done using MATLAB and Simulink.