Study of Hybrid Intelligent Controller for Interior Permanent Magnet Synchronous Motor Drive

Abstract

In this study, Genetic Algorithm (GA) based high performance control of synchronous motor is proposed whose rotor flux is estimated by evolutionary algorithm learning based Artificial Neural Network (ANN). Fast dynamic speed response is obtained through maintaining the rotor flux constant as in the case of field orientation realistic representation in the analysis. Genetic Algorithm (GA) based proportional- integral (PI) controller tuning is used for getting optimized gain coefficients of PI controller which also help us to get fast speed response synchronous motor drive. The performance of the drive system with GA based PI controller is compared with general algorithm based PI controller. A high performance simple speed estimator is presented in here. There is no speed fluctuation in the speed response of GA based synchronous motor drive under steady state condition when K is fixed whereas a little bit speed fluctuation is presented in the GA based synchronous motor drive under steady state condition when K is variable. In general, there is no speed fluctuation in the speed response of general algorithm based PI controller. But Genetic Algorithm is more fast performance than general algorithm. A simulation model of the drive system is developed and used in this study. The motor equations are written in rotor fixed d-q reference frame. A Proportional plus Integral (PI) controller is used to process the speed error to generate the reference torque current. The RNN estimator is used to estimate stator flux components along the stator fixed stationary axes (α-β) Hysteresis current controller block controls the switching of the three phase inverter to apply voltage to the motor stator. Numerical simulation is carried out in order to verify the effectiveness of the proposed control system. Simulation studies show that the proposed RNN estimator can be used to accurately measure the motor fluxes and rotor angle over a wide speed range. The robustness of the drive system is tested for different operating conditions, i.e., sudden load torque change, parameter deviation, speed reversal, ramp change of speed, load disturbance, presence of computational error, etc. The control system is found to work acceptably under these conditions. It is also simple and low cost to implement in a practical environment.