• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.794-796
• /
• 2004

In the evaluation of performance for the algorithm of sensorless speed control, the ability of speed control in low speed range and starting is important points. First of all, stability of low speed control is highly required in the application which needs high performance in speed control. For this requirement, this paper represents simple method to estimate the rotor position by comparing reference linkage flux with it's estimation. In the estimation of linkage flux, this paper uses voltage-current model for increasing the performance of speed control in low speed range.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.56 no.2
• /
• pp.74-82
• /
• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning control fuzzy neural network (AFLC-FNN) controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. 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-FNN controller. Also, this paper proposes speed control of IPMSM using AFLC-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 proposed control algorithm is applied to IPMSM drive system controlled AFLC-FNN controller, the operating characteristics controlled by efficiency optimization control are examined in detail.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.1251-1253
• /
• 2004

This paper is proposed a fuzzy neural network controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. 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 SPMSM using neuro-fuzzy control(NFC) and estimation of speed 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. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.55 no.2
• /
• pp.89-97
• /
• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-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. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN 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 IPMSM using LM-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 proposed control algorithm is applied to IPMSM drive system controlled LM-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 LM-FNN and ANN controller.

• Journal of Advanced Marine Engineering and Technology
• /
• v.28 no.4
• /
• pp.641-647
• /
• 2004

By most sensorless speed control schemes for induction motor. the control performances in high speed range are good, but it is difficult to obtain satisfactory results in low speed region. This paper proposes a new method controlling the low and the high speed regions separately to attain the stable operation in the overall range. The current error compensation method, in which the controlled stator voltage is applied to the induction motor so that the error between stator currents of the numerical model and the actual motor can be forced to decay to zero as time proceeds. is used in the low speed region In the high speed region. the method with adaptive observer is utilized. This control strategy contains an adaptive state observer for flux estimation. The rotor speed can be calculated from the rotor flux and the motor currents. The experimental results indicate good speed and load responses from the very low speed range to the high, and also show accurate speed changing performance between the low and the high speed range.

• Proceedings of the KIPE Conference
• /
• 2005.07a
• /
• pp.542-545
• /
• 2005

This paper proposes an algorithm for the moment of inertia estimation. The algorithm finds the moment of inertia observing the position error signal, which contains an error information of moment of inertia, generated by speed observer. Moreover, the proposed algorithm is easily realized in the observer -based speed detection method. The experimental results are also presented to confirm the performance of moment of inertia estimation method. The results show that the moment of inertia converges to the actual value with the proposed method.

• Journal of the Society of Naval Architects of Korea
• /
• v.55 no.4
• /
• pp.313-320
• /
• 2018

As ships operating on the Arctic route are exposed to various ice environments such as level ice, pre-swan, pack ice, ice ridge and brash ice, it is essential to estimate the ice resistance according to the ice environment. Methods for estimating the ice resistance include a method using mathematical model, numerical simulation, and a method using empirical formula. In this study, empirical formulas are used to estimate the ice resistance. The purpose of this study is to develop the ice resistance and attainable speed estimation program(I-RES) for brash ice. To develop the Brash ice attainable speed estimation algorithm, the environmental characteristics of the brash ice were analyzed, and the results of I-RES were evaluated by comparing the model test results of brash ice. The accuracy of I-RES for brash ice is around 20% in this study but it will be more developed near future with accumulating more model test results and calculation results.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.9
• /
• pp.4240-4258
• /
• 2016

A practical iterative channel estimation technique is proposed for the multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system in the high-speed mobile environment, such as high speed railway scenario. In the iterative algorithm, the Kalman filter and data detection are jointed to estimate the time-varying channel, where the detection error is considered as part of the noise in the Kalman recursion in each iteration to reduce the effect of the detection error propagation. Moreover, the employed Kalman filter is from the canonical state space model, which does not include the parameters of the autoregressive (AR) model, so the proposed method does not need to estimate the parameters of AR model, whose accuracy affects the convergence speed. Simulation results show that the proposed method is robust to the fast time-varying channel, and it can obtain more gains compared with the available methods.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.68 no.3
• /
• pp.430-438
• /
• 2019

For a pump or a compressor motor, a high periodic load torque variation is induced by the mechanical works, and it causes system vibration and noise. To minimize these problems, load torque compensation method, injecting periodic torque current, could be utilized. However, with the sensorless control method, which is usually utilized in the pump and compressor for low cost, the periodic torque current degrades the accuracy of the rotor position estimation owing to the inductance variation. This paper analyzes the rotor position and speed estimation error of sensorless control method with constant motor parameters under period loading. Assuming the constant speed by the accurate load torque compensation, the speed error equation is derived in frequency domain with inductance depending on the stator current. Further, it is also shown that the rotor position error could be minimized by compensating the inductance variation. The simulation and experimental results verify that the derived speed error model and the validity of the inductance compensation method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.7
• /
• pp.1241-1249
• /
• 2007

Lately, many approaches of speed sensorless control method for Interior Permanent Magnet Synchronous Motor(IPMSM) ha, been developed. This paper proposes a novel sensorless algorithm for speed estimation of IPMSM. First of all, proposes sensorless method estimates flux of rotor using foundational voltage equation of IPMSM and then estimates position and speed of rotor using Phase Locked Loop(PLL). Proposed sensorless algorithm demonstrated through simulation using Matlab simulink and experiment.