• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2007.11a
• /
• pp.307-312
• /
• 2007

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN) controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of induction motor using FLC-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 error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled FLC-FNN and ANN controller, Also, this paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.

• Journal of Power Electronics
• /
• v.12 no.1
• /
• pp.67-74
• /
• 2012

According to electrical output up rating of a permanent magnet motor and request to operate for a large variable speed range, resonance between structural natural vibration and electromagnetic force inside the motor can take place and make noise. This paper describes the mechanism of a resonance between them and noise lowering procedure by frequency shift when they are applied to the reluctance torque largely employed new motor named Permanent magnet Reluctance Motor (PRM).

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.9
• /
• pp.1552-1554
• /
• 2008

Recently, sensorless induction motor drives have been much studied due to several advantages. Sensorless drives eliminate the additional mounting space, increase the reliability in harsh environments, and reduce the cost of a motor. This paper investigates an improved sliding mode observer for the sensorless speed control of an induction motor. The proposed control strategy is the sliding mode observer with a variable boundary layer for a low-chattering and fast-response control. The proposed sensorless-algorithm is verified through the simulation and experimentation.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.42 no.6
• /
• pp.77-82
• /
• 2005

This paper is proposed fuzzy neural network(FNN) and artificial neural network(ANN) based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed control and estimation of speed of induction motor using fuzzy and neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the experimental results to verify the effectiveness of the new method.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.10 no.3
• /
• pp.282-289
• /
• 2005

This paper proposes a fuzzy neural network controller based on the vector control for interior permanent magnet synchronous motor(IPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability This paper does not oかy presents speed control of IPMSM using neuro-fuzzy control(NFC) but also speed estimation using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. Thus, it is presented the theoretical analysis as well as the analysis results to verify the effectiveness of the proposed method in this paper.

• Journal of Institute of Control, Robotics and Systems
• /
• v.2 no.4
• /
• pp.351-361
• /
• 1996

A variable structure controller is developed for an AC servo motor used in CNC milling machines. The designed controller is implemented as an outer loop controller to a factory designed motor-servopack system. The robustness parameter is tuned for a fast response when the speed tracking error is large, while it is tuned for small oscillations when the speed tracking error is small. The designed controller is installed on a CNC machine using a PC. Cutting experiments show improved performance over the factory-designed controller.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.782-784
• /
• 1999

This paper deals with speed control of DC-servo motor using a Back-Propagation(BP) Learning Algorism and a PID controller Conventionally in the industrial control, PID controller has been used. But the PID controller produced suitable parameter of each system and also variable of PID controller should be changed enviroment, disturbance, load. So this paper revealed for experimental, a neural network and a PID controller combined system using developed speed characters of a Variable Load DC-servo motor. The parameters of the plant are determined by neural network perform on on-line system after training the neural network on off-line system.

• Proceedings of the KIPE Conference
• /
• 1998.07a
• /
• pp.361-365
• /
• 1998

In this paper, the sliding mode variable structure-fuzzy(SMVS-F) control algorithm is applied to speed controller for field oriented induction motor drive system. According to the principle of sliding mode variable structure-fuzzy adjustable speed control scheme, the proposed algorithm shows good performances which are reducing chattering, robustness against parameter variation in induction motor drive. The validity of the proposed control scheme is verified by computer simulation using SIMULINK.

• Journal of Power System Engineering
• /
• v.3 no.2
• /
• pp.70-78
• /
• 1999

The vector controlled induction motor(I.M) with speed sensor has been widely used for variable speed drive systems. In these application fileds, speed sensorless control are expected strongly to progress reliability, simplicity and cost performance of I.M and to expand its application part. This paper describes a novel speed sensorless control method of I.M based on feedforward quick torque response control technique. Especially, this paper aimed at the realization of sensorless control in the very low speed region, The proposed method can be formulated simply from a motor circuit equation and conducted easily by detecting primary motor currents and a voltage command at every sampling time. Throughout some results of numerical simulations with the assumption of using a pulse width modulation(PWM) voltage source inverter, the validity of the method was successfully confirmed.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.561-564
• /
• 2004

This paper proposes a new application of Kalman filter to estimate speed sensorless DC motor. Kalman filter can estimate the system state variables accurately; even the system input is disturbed with noise. In the design, the mathematical model of DC motor in discrete state-space form will be created; the speed of DC motor which is considered as state variable and can be estimated by using Kalman filter. In the experiment; TMS320C31 floating point digital signal processor is used for hardware implementation, the input is disturbed with/and without white noise in the experiment. The experimental results show the speed of DC motor which is estimated by Kalman filter has good accuracy when compared with the results from tacho-meter.