• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.2
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• pp.135-141
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• 2012

PMSM(Permanent Magnet Synchronous Motor) has periodic torque ripple from the cogging torque and load conditions. This paper proposes the modified PID speed controller to reduce the speed ripple of the PMSM. The proposed modified PID controller uses a selective D(Differential) control term according to the speed error and the differential of the speed error. The proposed speed controller produces an additional torque reference such as torque compensator based on PI controller according to the speed error and the differential of the speed error, and it can reduce the vibration of the conventional D-control term with reduced speed ripple. Since the additional torque reference of the proposed speed controller is changed by the sign of the speed error and the differential of the speed error, a simple function to determine the sign of the error is used to produce the compensated torque. The proposed control scheme is verified by the computer simulation and the experiments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.8
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• pp.1217-1223
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• 2015

In this paper, we study the problem of torque ripple minimization in Brushless DC Motors (BLDC) and proposes a disturbance observer (DOB) based controller in order to efficiently reduce the torque ripple. In the DOB based control system, an equivalent disturbance (plant disturbance and effect of modelling error) is cancelled by its estimate. When the DOB controller is applied to BLDC motors, the effect of inverter switching is considered as an equivalent disturbance and to be cancelled by the DOB controller. Through computer simulations, it is shown that the performance of the proposed DOB controller is superior to that of the conventional PI controller. In the case where the numerical values of resistance and inductance are not known exactly, it is shown that the proposed DOB controller achieves better performance than the PI controller.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.530-532
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• 1998

In this paper, We Designed the torque compensatory controller for robot manipulator using modified chaotic neural networks with self feedback loop. The proposed torque compensatory controller compensate torque of the PD controller. In order to estimate the proposed controller, we implemented to the Cartesian space control of three-axis PUMA robot and compared the simulation results with recurrent neural networks(RNNs) controller. Simulation results show that the learning error drastically decrease at on-line learning. The proposed CNNs controller shows much better control performance and shorter processing time compared to the recurrent neural network controller in the robot trajectory control.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1177-1186
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• 2017

This paper presents a torque ripple reduction method of direct torque control (DTC) using fuzzy controller with optimal selection strategy of voltage vectors in a five-phase induction motor. The conventional DTC method has some drawbacks. First, switching frequency changes according to the hysteresis bands and motor's speed. Second, the torque ripple is rapidly increased in long control period. In order to solve these problems, some/most papers have proposed torque ripple reduction methods by using the optimal duty ratio of the non-zero voltage vector. However, these methods are complicated in accordance with the parameter. If this drawback is eliminated, the torque ripple can be reduced compared with conventional method. In addition, the DTC can be simply controlled without the use of the parameter. Therefore, the proposed algorithm is changing the voltage vector insertion time by using the designed fuzzy controller. Also, the optimized voltage vector selection method is used in accordance with the torque error. Simulation and experimental results show effectiveness of the proposed control algorithm.

• Proceedings of the KIEE Conference
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• 1997.07f
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• pp.2078-2081
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• 1997

IP speed controller is used as a main controller and it makes the system low overshoot and easy controllability. Load torque is estimated by Kalman filter algorithm and parameter controller is used against a rotor inertia negative variations. Parameter Controller (PC) is equipped with a torque observer implemented by software of a digital signal Processor. PC is a parameter controller which selects a moment of inertia J in responding to a load torque to control the system response.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.318-319
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• 2008

In this paper, a steering feel simulator is designed and implemented using a steering wheel with a rotation angle sensor, torque sensor, and an ac motor for the generation of the required torque. The controller in the simulator consists of a 16-bit micro-controller, a D/A converter and A/D converters. The main objective of the controller is to perform torque control where the reference torque is calculated from the torque map for both the vehicle velocity and the wheel sensor cutout. It is shown via the experimentation using the proposed simulator that the simulator output performance can be easily understood for the variation of vehicle parameters or controller parameters.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.257-259
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive learning mechanism-fuzzy neural network(ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and 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. The proposed control algorithm is applied to SynRM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• Journal of Power Electronics
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• v.11 no.2
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• pp.140-147
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• 2011

In this paper, a novel position controller based on state feedback and feed-forward is proposed. Traditional position and speed controllers are replaced by a single controller with the position and speed as state feedbacks, and the position command and load torque as feed-forwards. The feedback and feed-forward gains are obtained by analytic modeling and design. The load torque, rotor speed and position are estimated by an observer based on a Kalman filter (KF) with a low resolution mechanical position sensor. Feed-forward compensation by an estimated load torque is used to improve the dynamic performance during load torque changes.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.9
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• pp.26-35
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• 2010

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. This controller is controlled speed and current using hybrid PI(HBPI) controller and estimation of speed using ANN. Also, this paper is proposed maximum torque control of induction motor using slip angular speed and current condition at widely speed range. The performance of the proposed induction motor drive with maximum torque control using HBPI controller is verified by analysis results at dynamic operation conditions.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.778_779
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• 2009

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. The paper is proposed maximum torque control of induction motor drive using adaptive learning neuro fuzzy controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d, q axis current $_i_{ds}$, $i_{qs}$ for maximum torque operation is derived. The proposed control algorithm is applied to induction motor drive system controlled adaptive learning neuro fuzzy controller and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the adaptive learning neuro fuzzy controller and ANN controller.