This paper presents a simplified fuzzy logic based speed controller of an interior permanent synchronous motor (IPMSM) drive for maximum torque per ampere (MTPA) of stator current with inherent nonlinearities of the motor. The fundamentals of fuzzy logic algorithms as related to motor control applications are illustrated. A simplified fuzzy speed controller for the IPMSM drive has been found to maintain high performance standards with a much simpler and less computation intensive implementation than a nonsimplified fuzzy based algorithm.Contrary to the conventional control of IPMSM with d-axis current equal to zero, a non-linear expression of d-axis current has been derived and subsequently incorporated in the control algorithm for maximum torque operation. The efficacy of the proposed simplified fuzzy logic controller based IPMSM drive with MTPA is verified by simulation as well as experimentally at dynamic operating conditions. The simplified FLC with MTPA is found to be robust for application in the IPMSM drive. The complete vector control scheme is implemented in real-time using a digital signal processor (DSP) controller board DS 1102 in a laboratory 1 hp interior permanent magnet synchronous motor.

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This paper presents a laboratory testing of genetic algorithm (GA) based self-tuned PI controller far the speed control of interior permanent magnet synchronous motor (IPMSM). A radial basis artificial neural network function is used for on-line tuning of the PI controller. CA has been used in this work in order to obtain the optimized values of the PI constants for precise speed control. An performance index has been developed using GA, whose minimum value ensures zero steady-state error, minimum speed deviation and minimum settling time of the IPMSM drive. The initial values of the radial basis function network (RBFN) are obtained through off-line learning. Training data for off-line learning are generated hy simulating the IPMSM drive under various operating conditions and uncertainties. For on-line implementation, the PI constants are tuned hy updating lhe parameters of the RBFN maintaining the genetic performance index at its minimum value. In real time implementation, lhe proposed controller has been realized using a digital signal processor (DSP) board DSllOZ for a lhp laboratory IPMSM. The control algorithm is written in C++, compiled and then down loaded to the DSP hoard. The agreement between the simulation and test results confirms the effectiveness of the proposed controller for the vector control of the IPMSM.