• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.614-616
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• 2004

It is waste of time in industrial plant that the PI controller gain tuning. The PI controller has many trial-and-error steps for gain design. This paper proposes the optimum gain design of PI controller using a speed estimation in sensorless vector-control. In this method, a degree of stability and Hurwitz theory are applied and the controller gain is expressed by system parameters.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.3
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• pp.214-219
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• 2003

This paper includes real-time tractive force estimation method using standard vehicle sensors such as wheel speed, brake pressure, throttle position, engine speed, and transmission carrier speed sensor. Engine map, torque converter lookup table, shaft torque observer, and brake gain adaptation method are used to estimate the tractive force. To verify this estimator, measurement which uses strain-based brake torque sensor and estimation results are presented. All results was performed using a real vehicle in a real-time.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.268-272
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• 1989

To avoid the use of position sensor or flux sensor in a field oriented induction machine drive system, the terminal quantities are often used to estimate the rotor flux. Since the estimation involves the leakage inductance of the machine, the performance of such systems is sensitive to the variations of leakage. Since estimation of the stator flux is independent of the leakage, the steady state performance of the stator flux oriented system is insensitive to the leakage inductance. In this paper, the torque response of stator flux oriented system is compared to that of rotor flux oriented system by digital simulation. And induction motor sensor less speed control by stator flux oriented method is developed. The performance of the speed estimation is showed by digital simulation.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.4
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• pp.199-205
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• 2012

This paper propose a improved parameter estimations of five-phase squirrel-cage induction motor(IM) for speed control system on field oriented control(FOC). In order to high performance control of ac the motors using a FOC and DTC(direct torque control) algorithm, there are required precise motor parameters for slip calculation, flux observer, controller gain, rotor position and speed estimation, and so on. We are suggest a estimation method of the motor parameters that developing five-phase squirrel-cage IM have a stator of concentrated winding for experimental. There are results of stator winding test, no-load test, locked rotor test, and obtained equivalent circuits using manufactured experimental apparatus. For presenting the superior performance of the speed control system in adapted the parameters, experimental results are presented using a 32-bit fixed point TMS320F2812 DSP with 1.5[KW] IM.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.17-19
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• 2005

This paper considers the design and implementation of novel technique of speed estimation and control for IPMSM using learning mechanism-fuzzy neural network(LM-FNN) and artificial neural network (ANN) control. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid Intelligent control

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1166-1176
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• 2017

Estimation errors of the rotor speed and position in sensorless control systems of Permanent Magnet Synchronous Motors (PMSM) will lead to low efficiency and dynamic-performance degradation. In this paper, a parallel-type extended nonlinear observer incorporating the nominal parameters is constructed in the stator-fixed reference frame, with rotor position, speed, and the load torque simultaneously estimated. The stability of the extended nonlinear observer is analyzed using the indirect Lyapunov's method, and observer gains are selected according to the transfer functions of the speed and position estimators. Taking into account the parameter inaccuracies issue, explicit estimation error equations are derived based on the error dynamics of the closed-loop sensorless control system. An equivalent flux error is defined to represent the back Electromotive Force (EMF) error caused by the inaccurate motor parameters, and a compensation strategy is designed to suppress the estimation errors. The effectiveness of the proposed method has been validated through simulation and experimental results.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.808-810
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• 1996

In this paper, neural network, which has learning capability, is used for fault type classification and fault section estimation for high speed relaying. The potential of the neural network approach is demonstrated by simulation using ATP. The instantaneous values of voltages and currents are used the inputs of neural networks. This approach determines the fault section directly. In this paper, back-propagation network(BPN) is used for fault type classification and fault section estimation and can use for high speed relaying because it determines fault section within a few msec.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.114-115
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• 2014

This paper proposes a new speed and position estimation method for PMSM(Permanent Magnet Synchronous Motor) using low-resolution hall-effect sensors. In general, there are a variety of sensors to estimate rotor position for PMSM such as resolvers, and encoders. Position detection using hall-effect sensors that detect the flux of the rotor for rotor position is excellent method in terms of cost and space, but has low-resolution. To overcome this problem, this paper proposes a new speed and position estimation observer. The performance of the observer has been verified by simulations carried out using Matlab/Simulink.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.612-619
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• 2005

A robust adaptive speed sensorless induction motor direct torque control (DTC) using a neural network (NN) is presented in this paper. The inherent lumped uncertainties of the induction motor DTC system such as parametric uncertainty, external load disturbance and unmodeled dynamics are approximated by the NN. An additional robust control term is introduced to compensate for the reconstruction error. A control law and adaptive laws for the weights in the NN, as well as the bounding constant of the lumped uncertainties are established so that the whole closed-loop system is stable in the sense of Lyapunov. The effect of the speed estimation error is analyzed, and the stability proof of the control system is also proved. Experimental results as well as computer simulations are presented to show the validity and efficiency of the proposed system.

• Proceedings of the KIEE Conference
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• 1997.07f
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• pp.2113-2116
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• 1997

The speed sensorless vector control of induction motor using the rotor speed and flux estimation is widely used. In practice, these schemes depend on the accurate parameters of the machine. If in the vector control scheme an inaccurate parameter of induction motor due to skin effects and to temperature variations is used. it is difficult to achieve correct field orientation. From this reason. we propose robust speed sensorless vector control of induction motor against the variations of parameter and disturbance by using extended Kalman filter. For speed and rotor flux estimation. conventional adaptive flux observer is applied. extended Kalman filter which is correctly capable of estimating rotor flux and load by eliminating virtually influences of structural noises is proposed. Simulation results show the effectiveness of the control strategy proposed here for the induction motor drives.