• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.600-608
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• 2014

This paper presents estimation of d-axis and q-axis inductance of an interior permanent magnet synchronous motor (IPMSM) by using an extended Kalman filter (EKF). The EKF is widely used for control applications including the motor sensorless control and parameter estimation. The motor parameters can be changed by temperature and air-gap flux. In particular, the variation of the inductance affects torque characteristics like the maximum torque per ampere (MTPA) control. Therefore, by estimating the parameters, it is possible to improve the torque characteristics of the motor. The performance of the proposed estimator is verified by simulations and experimental results based on an 11kW PMSM drive system.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1824-1827
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• 1997

Majority of industrial robots are controlled by a simple joint servo control of joint actuators. In this type of control, the performance of control is influenced greatly by the joint interaction torques including Coriolis and centrifugal forces, which act as disturbance torques to the control system. As the speed of the robot increases, the effect of this disturbance torque increases, and makes the high speed-high precision control more difficult to achieve. In this paper, the joint disturbance torque of robots is analyzed. The joint disturbance torque is defined using the coefficients of dynamic equation of motion, and for the case of a 2DOF planar robot, the conditions for the maximum joint disturbance torques are identified, and the effect of link parameters and joint variables on the joint disturbance torque are examined. Then, a solutioin to the optimal path placement problem is proposed that minimizes the joint disturbance torque are examined. then, a solution to the optimal path placement problem is proposed that minimizes the joint disturbance torque during a straight line motion. the proposed method is illustrated using computer simulation. the proposed solution method cna be applied to the class of robots that are controlled by independent joint sevo control, which includes the vast majority of industrial robots. By minimizing the joint disturbacne torque during the motion, the simple joint servo controlled robot can move with improved path tracking accuracy at high speed.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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• pp.313-318
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive fuzzy neural network controller and artificial neural network(ANN). This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using Adaptive-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper reposes speed control of IPMSM using Adaptive-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is a lied to IPMSM drive system controlled Adaptive-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the Adaptive-FNN and ANN controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.5
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• pp.32-43
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive learning fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive learning fuzzy neural network and artificial neural network.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.431-434
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• 2002

This paper presents an implementation of direct torque control(DTC) of Reluctance Synchronous Motor(RSM) with an efficiency optimization using the 32bit DSP TMS320C31. The influence of iron loss can not neglected as high speed and precision torque control of RSM, so the optimal current ration between torque current and exiting current analytically derived to drive RSM at maximum efficiency For RSM, torque dynamics can be maintained even with controlling the flux level because the generated torque is direct]y proportional to the stator current. The experimental results for an RSM are presented to validate the applicability of the proposed method. The developed control system is shown high efficiency features with 1.0Kw RSM having 2.57 ratio of d/q reluctance.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.12
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• pp.82-87
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• 2011

In this paper, a sensorless speed control system is designed for the next generation high speed railway at zero and low speed region. The applied vector control scheme is a maximum torque per ampere(MTPA) method to utilize reluctance torque of IPMSM. The designed sensorless control scheme is a rotating high frequency voltage signal injection method. To verify the designed system, a simulator for the vector controller and sensorless controller is implemented using Matlab/simulink.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.9
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• pp.118-123
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• 2010

The selection of flux level in the maximum torque control of stator flux-oriented induction motor drives in the field weakening region is dependent on stator resistance and inductances. This paper presents parameter detuning effects of stator flux-oriented control drives in the field weakening region. The detuning effects of stator resistance and rotor leakage inductance are analyzed. The decrease of torque and the flux control lost by the detuning of inductance are shown in the simulation results.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.20-22
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• 2005

In this paper, we propose fuzzy-neural network controller that combines a fuzzy control and the Neural Networks for high performance control of induction motor drive, Also, this paper is proposed control of maximum torque per ampere of induction motor. This strategy is proposed which is simple in structure and has the honest goal of minimizing the stator current magnitude for given load torque. The performance of the proposed induction motor drive with maximum torque control using fuzzy-neural network controller is verified by simulation at dynamic operation conditions.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1223-1230
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• 2017

The back emf harmonics of a permanent magnet (PM) synchronous motor is a major source of torque ripple. For torque control applications including column fitted MDPS (motor driven power steering) systems, it is essential to reduce the mechanical vibrations due to torque ripples at low speeds. In this paper, a torque ripple reduction algorithm for interior PM synchronous motors is proposed. The harmonic currents that cancel the $6^{th}$ order torque harmonic are added to the nominal dq currents for MTPA (maximum torque per ampere) operation. The compensated harmonic currents are derived from flux linkage harmonics based on a FFT analysis of the back emf harmonics. Simulation and experimental results verify that the $6^{th}$ order torque harmonic and THD of the torque ripple are reduced by compensating the dq harmonic currents.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.342-348
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• 1998

A majority of industrial robots are controlled by a simple joint servo control of joint actuators. In this type of control, the performance of control is greatly influenced by the joint interaction torques including Coriolis and centrifugal forces, which act as disturbance torques to the control system. As the speed of the robot increases, the effect of this disturbance torque increases, and hence makes the high speed - high precision control more difficult to achieve. In this paper, the joint disturbance torque of robots is analyzed. The joint disturbance torque is defined using the coefficients of dynamic equation of motion, and for the case of a 2 DOF planar robot, the conditions for the minimum and maximum joint disturbance torques are identified, and the effect of link parameters and joint variables on the joint disturbance torque are examined. Then, a solution to the optimal path placement problem is propose that minimizes the joint disturbance torque during a straight line motion. The proposed method is illustrated using computer simulation. The proposed solution method can be applied to a class of robots that are controlled by independent joint servo control, which includes the vast majority of industrial robots.