• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2526-2531
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• 2005

A practical pinch torque estimator based on the Kalman filter is proposed for low-cost anti-pinch window control systems. To obtain the accurate angular velocity from Hall-effect sensor measurements, the angular velocity calculation algorithm is executed with additional procedures for removing the measurement noises. Apart from the previous works using the angular velocity estimates and torque estimates for detecting the pinched condition, the torque rate is augmented to the system model and the proposed pinch estimator is derived by applying the steady-state Kalman filter recursion to the model. The motivation of this approach comes from the idea that the bias errors in torque estimates due to the motor parameter uncertainties can be almost eliminated by introducing the torque rate state. For detecting the pinched condition, a systematic way to determine the threshold level of the torque rate estimates is also suggested via the deterministic estimation error analysis. Simulation results are given to certify the pinch detection performance of the proposed algorithm and its robustness against the motor parameter uncertainties.

• Proceedings of the KIEE Conference
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• 2001.07e
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• pp.47-52
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• 2001

This paper presents a high-performance control system for Reluctance Synchronous Motor (RSM) drives with direct torque control (DTC). The system consist of stator flux observer, rotor position/speed estimator, torque estimator, two hysteresis band controllers, an optimal switching look-up table, IGBT voltage source inverter, and F240/C31DSP controller by using fully integrated control software. The stator flux observer is based on the combined voltage and current model with stator flux feedback adaptive control that inputs are current and voltage sensing of motor terminal with estimated rotor angle for wide speed range. The rotor position is estimated by the observed stator flux-linkage space vector. The estimated rotor speed can be determinated by differentiation of the rotor position used only in the current model part of the flux observer for a low speed operating area. To prove the suggested control algorithm, we have a simulation and testing at actual experimental system. The developed digitally high-performance position sensorless control system are shown a good motion control response characteristic results and high performance features using 1.0Kw RSM.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1136-1139
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• 2000

The paper describes a new and rigorous mathematical model for the rotor field oriented system with induction motor which uses the estimated speed and rotor flux based on a Model Reference Adaptive System, as well as the real-time approach. The estimated speed and rotor flux is used for the speed and flux feedback control. The stability and the convergence of the estimator are improved on the basis of hyperstability theory for non-linear systems. The real-time controller and estimator are implemented with a sampling period of $926{\mu}s$ using a dual TMS320C44 floating-point digital signal processor. The validity of the proposed method is verified by simulation, and also, the sensorless control was tested on the propulsion system simulator, used for the development of Korean High-Speed Railway Train (KHSRT) [5].

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.1
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• pp.18-27
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• 2002

This paper presents an implementation of digital high-performance speed sensorless control system of an induction motor drives with Direct Torque Control(DTC). The system consists of closed loop stator flux and torque observer, speed and torque estimators, two hysteresis controllers, an optimal switching look-up table, IGBT voltage source inverter, and TMS320C31 DSP controller board. The stator flux observer is based on the combined current and voltage model with stator flux feedback adaptive control for wide speed range. The speed estimator is using the model reference adaptive system(MRAS) with rotor flux linkages for speed turning signal estimation. In order to prove the suggested speed sensorless control algorithm, and to obtain a high-dynamic robust adaptive performance, we have some simulations and actual experiments at low(20rpm) and high(1000rpm) speed areas. The developed speed sensorless system are shown a good speed control response characteristic, and high performance features using 2.2[kW] general purposed induction motor.

• Proceedings of the KIEE Conference
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• 2001.10a
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• pp.161-164
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• 2001

This paper presents a digital speed sensorless control system for Reluctance Synchronous Motor (RSM) drives with direct torque control (DTC). The system consist of stator flux observer, rotor speed estimator, torque estimator two hysteresis band controllers, an optimal switching look-up table. IGBT voltage source inverter, and TMS320C31DSP controller by using fully integrated control software. The stator flux observer is based on the combined voltage and current model with stator flux feedback adaptive control that inputs are current and voltage sensing of motor terminal with estimated rotor angle for wide speed range. The rotor speed is estimated by the observed stator flux-linkage space vector. The estimated rotor speed can be determinated by differentiation of the rotor position used only in the current model part of the flux observer for a low speed operating area. In order to prove the suggested speed sensorless control algorithm. There are some simulation and testing at actual experimental system. The developed digitally high- performance speed sensorless control system are shown a good speed control response characteristic results and high Performance features using 1.0Kw RSM.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.32-42
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• 2001

In this paper, a parameter estimator is developed for the plant model whose structure is represented by the Takagi-Sugeno model. The essential idea behind the on-line estimation is the comparison of the measured stated with the state of an estimation model whose structure is the same as that of the parameterized model. Based on the parameter estimation scheme, and indirect Model Reference Adaptive Fuzzy control(MRAFC) scheme is proposed to provide asymptotic tracking of a reference signal for the systems with uncertain for slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop systems. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.6
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• pp.1036-1055
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• 1992

In this paper the feedback linearization of the valve-controlled nonlinear hydraulic velocity control system and the implementation of the digital state feedback controller is studied. The $C^{\infty}$ nonlinear transfomation to the electro-hydraulic velocity control system, which transforms nonlinear system to linear equivalent one, is obtained. It is shown that this transformation is global one. The digital controller to this linearized model is obtained by using the one-step ahead state estimator and implemented to real plant. The proposed implementation method is easier than the other proposed methods and it is possible to control in real time. The experiment and simulation study show that the implementation of the digital state feedback controller based on the feedback linearized model is successful..

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.457-476
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• 2000

In this study, we have proposed a sampling method and an estimation method for efficiently estimating the mean of a population which has a linear trend. These methods involve drawing a sample by the so-called "centered balanced systematic sampling", which is an extension of systematic sampling, and then estimating the population mean with an adjusted estimator, not with the sample mean itself. We used the concept of interpolation in determining the adjusted estimator.\Ve compared the efficiency of the proposed estimator with those of the estimators from existing methods, under the expected mean square error criterion based on the infinite superpopulation model introduced by Cochran(1946). The proposed method is for use in the case when the sample size n(2 5) is an odd number and k(the reciprocal of the sampling fraction) is an even number. A good result was also obtained in an example using computer simulation. simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.544-547
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• 1997

In this paper, we propose a dynamic localization method using a rotating sonar and a map. The proposed method is implemented by using extended Kalman filter. The state equation is based on the encoder propagation model and the encoder error model, and the measurement equation is a map-based measurement equation using a rotating sonar sensor. By utilizing sonar beam characteristics, map-based measurements are updated while AMR is moving continuously. By modeling and estimating systematic errors of a differential encoder, the position is successfully estimated even the interval of the map-based measurement. Monte-Carlo simulation shows that the proposed global position estimator has the performance of a few millimeter order in position error and of a few tenth degrees in heading error and of compensating systematic errors of the differential encoder well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.87-107
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• 2015

Considering the localization estimation issue in mixed line-of-sight (LOS)/non-LOS(NLOS) environments based on received signal strength (RSS) measurements in wireless sensor networks, a grid-based correlation method based on the relationship between distance and RSS is proposed in this paper. The Maximum-Likelihood (ML) estimator is appended to further improve the localization accuracy. Furthermore, in order to reduce computation load and enhance performance, an improved recursively version with NLOS mitigation is also proposed. The most advantages of the proposed localization algorithm is that, it does not need any prior knowledge of the propagation model parameters and therefore does not need any offline calibration effort to calibrate the model parameters in harsh environments, which makes it more convenient for rapid implementation in practical applications. The simulation and experimental results evidence that the proposed localization algorithm exhibits good localization performance and flexibilities for different devices.