• Journal of Institute of Control, Robotics and Systems
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• v.16 no.4
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• pp.329-334
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• 2010

This paper presents the design of a neural network based adaptive control for missile. Acceleration of missile by tail fin control cannot be controllable by DMI (Dynamic Model Inversion) directly because it is non-minimum phase system. To avoid the non-minimum phase system, dynamic model inversion is applied with output-redefinition method. In order to evaluate performance of the suggested controllers we selected the three cases such as control surface fail, control surface loss and wing loss for model uncertainty. The corresponding aerodynamic databases to the failure cases were calculated by using the Missile DATACOM. Using a high fidelity 6DOF simulation program of the missile the performance was evaluates.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.3
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• pp.149-156
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• 2011

Transmission delay and packet loss induced by a communication network can degrade the control performance and, even make the system unstable. This paper presents a method for compensating transmission delay and packet loss in a networked control system for unmanned underwater vehicle. The proposed method is based on Lagrange interpolation in order to satisfy the requirements of simplicity and model-independency. In this work, the lost/delayed data are estimated in real time by only using the past data without requiring any mathematical model of the controlled system. Consequently, the proposed method can be implemented independent of the controlled system, and also it can achieve fast and accurate compensation performance. The performance of the proposed technique is evaluated by numerical simulations with an unmanned underwater vehicle.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.49 no.2
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• pp.116-122
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• 2000

In the high speed range for salient type synchronous reluctance motor, the effect of iron loss can not be negligible. We have investigated the voltage equations including iron loss from the model that is added the equivalent iron loss in the equivalent inductance in series. In this paper, we derive Ld linear approximate equation from saturation range of Ld, Lq vs applied voltage characteristics and obtain equations including saturation and iron loss related to maximum torque control using Ld. The effect of saturation and iron loss is investigated under maximum torque control. And we show that the proposed maximum torque control scheme achieves the desired performances through experimental results.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.470-479
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• 2016

The model based method for energy saving of the separately excited DC motor drive system is proposed in the paper. The accurate power loss model is necessary for this method. Therefore, the adaptive tabu search algorithm is applied to identify the parameters in the power loss model. The field current values for minimum power losses at any load torques and speeds are calculated by the proposed method. The rule based controller is used to control the field current and speed of the motor. The experimental results confirm that the model based method can successfully provide the energy saving for separately excited DC motor drive. The maximum value of the energy saving is 48.61% compared with the conventional drive method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.4
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• pp.128-135
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• 1998

In the high speed range for salient type synchronous reluctance motor, the effect of iron loss can not be negligible. In this paper, under he assumption that stator iron loss is generated from the equivalent eddy current in the stator, we derive the voltage equations including iron loss from the model that is added the equivalent iron loss in the equivalent inductance in series. The variation of iron loss is dependent on the increase of the operating frequency change for he worse a performance of the vector control system. As there is cross coupling between the d and q axes, it is hard to apply the vector control to the proposed model. Hence, we propose a decoupling current controller for including the effects of iron loss, And we show that the proposed vector control scheme achieves the desired performances through simulation results.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1356-1357
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• 2011

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. This paper proposes on-line efficiency optimization of IPMSM drive using fuzzy logic control(FLC) and the loss minimization method. In order to optimize the efficiency the loss minimization algorithm is developed based on motor model and operating condition. The d-axis armature current is utilized to minimize the losses of the IPMSM in a closed loop vector control environment. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. The proposed control algorithm is applied to IPMSM drive system and the operating characteristics controlled by the loss minimization method and FLC control are examined in detail.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.6
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• pp.490-495
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• 2020

The power loss of large-capacity systems using single-phase inverters has attracted considerable attention. In this study, optimal switching sequence model prediction control at a low switching frequency is proposed to reduce the power loss in a high-power inverter system, and a compensation method that can be utilized for model prediction control is developed to reduce errors in accordance with sampling values. When a three-level, single-phase inverter using a switching frequency of 600 Hz and a sampling frequency of 12 kHz is adopted, the power factor is improved from 0.95 to 0.99 through 3 kW active power control. The performance of the controller is also verified.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.3
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• pp.279-287
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• 2010

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning controller(AFLC). In order to optimize the efficiency the loss minimization algorithm is developed based on motor model and operating condition. The d-axis armature current is utilized to minimize the losses of the IPMSM in a closed loop vector control environment. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC. Also, this paper proposes speed control of IPMSM using AFLC1, current control of AFLC2 and AFLC3, and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled AFLC, the operating characteristics controlled by efficiency optimization control are examined in detail.

• Journal of Power Electronics
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• v.12 no.2
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• pp.305-316
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• 2012

The paper presents an accurate loss model based controller of an induction motor to calculate the optimal air gap flux. The model includes copper losses, iron losses, harmonic losses, friction and windage losses, and stray losses. These losses are represented as a function of the air gap flux. By using the calculated optimal air gap flux compared with rated flux for speed sensorless indirect vector controlled induction motor, an improvement in motor efficiency is achieved. The motor speed performance is improved using a fuzzy logic speed controller instead of a PI controller. The fuzzy logic speed controller was simulated using the fuzzy control interface block of MATLAB/SIMULINK program. The control algorithm is experimentally tested within a PC under RTAI-Linux. The simulation and experimental results show the improvement in motor efficiency and speed performance.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.652-657
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• 2004

As the real-time multimedia applications through Internet increase, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example meeting this necessity. The TCP-friendly (TFRC) is an UDP-based protocol that controls the transmission rate based on the available round transmission time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used for the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately.