• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.516-518
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• 1999

This paper presents an feed-forward neural network design instead PI controller for the speed control of an Induction Motor. The design employs the training strategy with Neural Network Controller(NNC) and Neural Network Emulator(NNE). Emulator identifies the motor by simulating the input and output map. In order to update the weights of the Controller. Emulator supplies the error path to the output stage of the controller using backpropagation algorithm. and then Controller produces an adequate output to the system due to neural networks learning capability. Therefore it becomes adjustable to the system with changing characteristics caused by a load. The speed control based on neural networks for induction motor is implemented by a vector controlled induction motor. The simulation results demonstrate that actual motor speed with neural network system well follows the reference speed minimizing the error and is available to implement on the vector control theory.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.42-53
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• 1996

The paper presents a speed control system of vector controlled induct- ion motor using neural networks. The main feature of proposed speed control system is a Neural Network Controller(NNC) which supplies torque current to induction motor and Neural Network Emulator(NNE) which captures the forward dynamics of induction motor. A back propagation training algorithm is employed to train the NNE and NNC. In order to determine the NNC output error, plant(induction motor) output error can be back propagated through the NNE. The NNC and NNE for speed control of vector controlled induction motor is carried out by TMS320C30 DSP and IGBT current regulated PWM inverter. Through computer simulation and experimental results, it is verified that proposed speed control system is robust to the load variation. (author). refs., figs.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.463-471
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• 1999

This paper presents a vector controlled induction motor is implemented by neural networks system compared with PI controller for the speed control. The design employed the training strategy with Neural Network Controller(NNC) and Neural Network Emulator(NNE) for speed. In order to update the weights of the controller First of all Emulator updates its parameters by identifying the motor input and output next it supplies the error path to the output stage of the controller using backpropagation algorithm, As Controller produces an adequate output to the system due to neural networks learning capability Vector controlled induction motor characteristics actual motor speed with based on neural network system follows the reference speed better than that of linear PI speed controller.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.251-254
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• 1995

The Fuzzy-Neural Controller is constructed to resolve some dificulties taking place in decision of membership functions, input and output gains and an inferenced method for desinging fuzzy logic controller. In addition Neural network emulator is used to emulate induction motor forward dynamics and to get error signal at fuzzy-neural controller output layer. Error signal is backpropagated through neural network emulator. A back propagation algorithm is used to train fuzzy-neural controller and emulator. The experimental results show that this control system can provide good dynamical responses.

• Journal of Power Electronics
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• v.15 no.1
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• pp.246-255
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• 2015

This paper presents experimental results and its assessment of a variable-speed wind power generation system (VSWPGS) using permanent magnet synchronous generator (PMSG) and boost chopper circuit (BCC). Experimental results are obtained by a test bench with a wind turbine emulator (WTE). WTE reproduces the behaviors of a windmill by using servo motor drives. The mechanical torque references to drive the servo motor are calculated from the windmill wing profile, wind velocity, and windmill rotational speed. VSWPGS using PMSG and BCC has three speed control modes for the level of wind velocity to control the rotational speed of the wind turbine. The control mode for low wind velocity regulates an armature current of generator with BCC. The control mode for middle wind velocity regulates a DC link voltage with a vector-controlled inverter. The control mode for high wind velocity regulates a pitch angle of the wind turbine with a pitch angle control system. The hybrid of three control modes extends the variable-speed range. BCC simplifies the maintenance of VSWPGS while improving reliability. In addition, VSWPGS using PMSG and BCC saves cost compared with VSWPGS using a PWM converter.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.173-183
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• 1998

In this paper, an auto-tuning method for fuzzy controller's membership functions based on the neural network is presented. The neural network emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and the reformed fuzzy controller uses for speed control of induction motor. Thus, in the case of motor parameter variation, the proposed method is superior to a conventional method in the respect of operation time and system performance. 32bit micro-processor DSP(TMS320C31) is used to achieve the high speed calculation of the space voltage vector PWM and to build the self-learning fuzzy control algorithm. Through computer simulation and experimental results, it is confirmed that the proposed method can provide more improved control performance than that PI controller and conventional fuzzy controller.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.254-255
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• 2012

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.11
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• pp.1564-1570
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• 2015

This paper intends to suggest a sensing circuit of PWM voltage for a motor emulator operated in the inverter. In the emulation of the motor using a power converter, it is necessary to measure instantaneous voltage at the PWM voltage loaded from the inverter. Using a filter can generate instantaneous voltage, while it is difficult to follow the rapidly changing inverter voltage caused by the propagation delay and signal attenuation. The method of measuring the duty of PWM using FPGA can generate output voltage from the one-cycle delay of PWM, while the cost of hardware is increasing in order to acquire high precision. This paper suggests a PWM voltage sensing circuit using the analogue system that shows high precision, one-cycle delay of PWM and low-cost hardware. The PWM voltage sensing circuit works in the process of integrating input voltage for valid time by comparing levels of three-phase PWM input voltage, and produce the output value integrated at zero vector. As a result of PSIM simulation and the experiment with the produced hardware, it was verified that the suggested circuit in this paper is valid.

• Proceedings of the KIPE Conference
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• 1998.11a
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• pp.1-4
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• 1998

In this paper, an auto-tuning method for fuzzy controller based on the neural network is presented. The backpropagated error of neural emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and used for speed control of switched reluctance motor. The experiments are performed to verify the capability of proposed control method on 6/4 salient type SRM. The results show that fuzzy-neural controller is suitable for wide speed range.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.4
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• pp.109-119
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• 1999

Switched Reluctance Motor(SRM) have been expanding gradually their awlications in the variable speed drives due to their relatively low cost, simple and robust structure, controllability and high efficiency. In this paper neural network theory is used to detemrine fuzzy-neural network controller's membership ftmctions and fuzzy rules. In addition neural network emulator is used to emulate forward dynamics of SRM and to get error signal at fuzzy-neural controller output layer. Error signal is backpropagated through neural network emulator. The backpropagated error of emulator offers the path which reforms the fuzzy-neural network controller's mmbership ftmctions and fuzzy rules. 32bit Digital Signal Processor(TMS320C31) was used to achieve the high speed control and to realize the fuzzy-neural control algorithm. Simulation and experimental results show that in the case of load variation the proposed control rrethcd was superior to a conventional rrethod in the respect of speed response.sponse.