Multi-objective genetic-fuzzy optimal design of PI controller in the indirect field oriented control of an induction motor

In this paper, a novel multi-objective optimization method based on a Genetic-Fuzzy Algorithm (GFA) is proposed. GFA is applied to optimize the five PI controller gains in the Indirect Field Oriented Control (IFOC) of an induction motor drive. The PI controller gains are designed to optimize the step response of the system. Rise-time, maximum over-shoot, settling time and steady state error are the objective functions. In this drive system, the simultaneous estimation of the rotor speed and time constant for a voltage source inverter-fed induction motor is discussed. The theory is based on the parallel Model Reference Adaptive Control System (MRAC). The vector control of the induction motor may be achieved in the rotor-flux-oriented frame. Furthermore, to eliminate the offset error caused by the change in the stator resistance, a fuzzy resistance is also designed. The simulation results of the new method for induction motor speed control is compared with the results obtained by the conventional method, which allows better performance.