• Proceedings of the KIEE Conference
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• 2002.04a
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• pp.79-81
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• 2002

This paper develops a adaptive fuzzy controller based fuzzy logic control for high performance speed controller in permanent magnet synchronous motor(PMSM) drives. In the proposed system, fuzzy control is used to implement the direct controller as well as the adaptation mechanism. The operation of the direct fuzzy controller and the fuzzy logic based adaptation mechanism is studied. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed adaptive fuzzy controller is confirmed by performance results for PMSM drive system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.2
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• pp.233-243
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• 1994

Generally, fuzzy PI controller that regulates the gains using fuzzy algorithm shows high performance in speed response. However, it has some problems to the load inertia variation, because the change of speed error(CE) is in a fixed range. As load inertia increases, CE is decreased and the usuage of fuzzy table is limited. Therefore, the output of the fuzzy controller has a limited range. This paper proposes an improved fuzzy PI controller. To reduce the speed overshoot, we adapt a control method that selects a proper CE range with respect to the load inertia variation. The proposed controller is applied to the vector controlled system with 2.2kW induction motor. Some simulation and experimental results are exhibited. With these results, we can easily find that proposed PI controller is more robust than the conventional fuzzy PI controller against the load inertia variation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.11
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• pp.1663-1668
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• 2017

A BLDC motor with higher number of phases has several advantages, compared to the conventional three-phase BLDC motors. It can reduce the commutation torque ripple and the iron loss without increasing the voltage per phase and increase the reliability and power density. Higher number of phases increase the torque-per-ampere ratio for the same machine volume and output power by widening the electrical conduction period. In this paper, the proposed seven-phase BLDC motor drive system is made into several functional modular blocks, so that it can be easily extended to other ac motor applications: back-EMF block, hysteresis current control block, pwm inverter block, phase current block, and speed/torque control block. Also in a system of BLDC motor drive, the PI controller has been widely used in the speed controller because of the simple implementation. To obtain a good speed response in a general drive system using the PI controller, the high bandwidth of a controller is established. therefore, in this paper, a Fuzzy controller is applied to the 7-phase BLDC motor drive system in order to improve the speed control performance. The Fuzzy controller is compared with a conventional PI controller through the experiment with respect to speed dynamic responses. These experimental results show that the Fuzzy controller of the 7-phase BLDC motor drive system is superior over the conventional PI controller. The algorithm using the Fuzzy controller can improve a comfortable ride in the field of high performance 7-phase BLDC motor drive applications.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1978-1983
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• 2011

BLDC motor is used in a wide variety of industrial and servo applications. Its features and advantages mainly consist in high value of torque/inertia ratio, high efficiency with speed range and high dynamic performance. This paper deals with the speed control of a trapezoidal type brushless DC motor using Fuzzy PI controller. The conventional PI controller has been widely used in industrial applications. If we select a optimal PI control gain, the PI controller shows very good control performance. But it is very difficult to find the optimal PI control gain. Fuzzy control does not need any model of plant and is basically adaptive and gives robust performance for plant parameter variation. Therefore the combinations of conventional PI controller and fuzzy controller seem to be very effective. This paper deals with PI controller with 4-rule based fuzzy controller. The proposed fuzzy PI controller increases the control performance of the conventional PI controller. Simulation and experimental results show that fuzzy PI controller has a good robustness regarding the improper tuned PI controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.5
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• pp.22-33
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• 2011

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjection with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid fuzzy controller for direct torque control(DTC) of induction motor drives. The speed controller is based on adaptive fuzzy learning controller(AFLC), which provide high dynamics performances both in transient and steady state response. Flux position, error in flux magnitude and error in torque are used as FLC state variables. The speed is estimated with model reference adaptive system(MRAS) based on artificial neural network(ANN) trained on-line by a back-propagation algorithm. This paper is controlled speed using hybrid fuzzy controller(HFC) and estimation of speed using ANN. The performance of the proposed induction motor drive with HFC controller and ANN is verified by analysis results at various operation conditions.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1009-1010
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• 2007

This paper presents Fuzzy-Neuro PI controller of IPMSM drive using fuzzy and neural-network. In general, PI controller in computer numerically controlled machine process fixed gain. To increase the robustness, fixed gain PI controller, Fuzzy-Neuro PI controller proposes a new method based fuzzy and neural-network. Fuzzy-Neuro PI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.390-393
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• 1999

For high performance ac drives, the speed sensorless vector control and a speed control algorithm base on the Fuzzy PI controller have received increasing attention. A Fuzzy PI controller is used for robust and fast speed control and space vector modulation method is used for PWM wave generation in this proposed system. The computer simulation results show that the proposed controller are more excellent control characteristics than conventional PI controller in transient-state response.

• Journal of Power Electronics
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• v.8 no.3
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• pp.210-216
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• 2008

This paper presents a classical speed controller (CSC) for vector controlled induction motors. The controller explores the use of a Fuzzy Logic controller in a classical form. The controller combines the advantages of the classical controller and the properties of intelligent controllers. The Fuzzy Logic controller idea is used to obtain the CSC output equation, whereby the CSC equation is based on the speed error and its change. The CSC parameters are calculated based on the motor mechanical equation and a predefined system performance. Once the CSC parameters are obtained, the defined speed performance can be achieved at all operating conditions. The application of the CSC to control the speed of a vector controlled induction motor is presented. Different induction motor ratings are used. Simulation results in all possible olperating conditions are presented. Results show that the CSC behaves as an expert controller to provide the predefined speed performance in all possible operating conditions. Based on the results obtained in this paper, the CSC is expected to become the ultimate solution for high-performance drives of the next generation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.9
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• pp.33-43
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• 2011

Since the fuzzy neural network(FNN) is universal approximators, the development of FNN control systems have also grown rapidly to deal with non-linearities and uncertainties. However, the major drawback of the existing FNNs is that their processor is limited to static problems due to their feedforward network structure. This paper proposes the recurrent FNN(RFNN) for high performance and robust control of SynRM. RFNN is applied to speed controller for SynRM drive and model reference adaptive fuzzy controller(MFC) that combine adaptive fuzzy learning controller(AFLC) and fuzzy logic control(FLC), is applied to current controller. Also, this paper proposes speed estimation algorithm using artificial neural network(ANN). The proposed method is analyzed and compared to conventional PI and FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1147-1150
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• 2001

This paper represents a realization of a fuzzy PI control method for a speed control of BLDC motor. In other words, the gains of the PI controller is tuned by a fuzzy logic controller. Simplified reasoning methods are used for fuzzy reasoning. Fuzzy logic speed controller is designed by using the high performance of DSPchip(TMS320F240). By experiment, it is confirmed that the speed of BLDC motor well follows an command speed in the load variables or speed variables.