Development and implementation of a hybrid intelligent controller for interior permanent magnet synchronous motor drive

A hybrid neuro-fuzzy scheme for the online tuning of a genetic based proportional-integral (PI) controller for interior permanent magnet synchronous motor (IPMSM) drives is presented in this paper. The proposed controller is developed for accurate speed control of the IPMSM drive under various system disturbances. In this work, initially different operating conditions are obtained based on motor dynamics incorporating uncertainties. At each operating condition, a genetic algorithm (GA) is used to optimize the PI controller parameters in a closed loop vector control scheme. In the optimization procedure, a performance index is developed to reflect the minimum speed deviation, minimum settling time and zero steady-state error. A fuzzy basis function network (FBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. The proposed FBFN based PI controller provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed controller is successfully implemented in realtime using a digital signal processor board DS 1102 for a laboratory 1 hp IPMSM. The efficacy of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. The proposed controller is found to be robust for applications In IPMSM drive.