• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.326-330
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• 2001

A nonlinear speed control for a permanent magnet (PM) synchronous motor using a simple disturbance estimation technique is presented. By using a feedback linearization, scheme, the nonlinear motor model can be linearized. To compensate an undesirable output performance under the mismatch of the system parameters and load conditions the controller parameters will be estimated by using a disturbance observer theory. Since only the two reduced-order observers are used for the parameter estimation, the observer designs are considerably simple and the computational load of the controller for parameter estimation is negligibly small. The proposed control scheme is implemented on a PM synchronous motor using DSP TMS320C31 and the effectiveness is verified through the comparative experiments.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.7
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• pp.696-705
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• 1991

This paper describes the procedures for pre-tuning and re-tuning the gains of PI controller based on output patterns -output error integral- of the unknown process which may not have any information, for example, system order, deadtime, time constant, etc. The key ideas of the proposed adaptive scheme are as follows. The scheme determines the initial gains by using ZNM (Ziegler-Nichols Method) with relay feedback, and then the adaptive algorithms by pattern recognition are introduced for re-runing the PI gains with on-line scheme whenever control conditions are changed. Because, among the various auto-tuning procedures, ANM with relay feedback has the difficulty in re-tuning with on-line and Bristol method has no comment on initial settings and has variables to pre-determine, which makes the algorithm comples, the proposed methods have the combined scheme with above two procedures to recover those problems. And this paper proposes a simple way to determine adaptive constant in Bristol method. To show the validity of the proposed method, an example is illustrated by computer simulation and a laboratory process, heat exchanger, is experimented.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.126-128
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• 2018

Solar array simulator (SAS) is a special DC power supply that regulates the output voltage or current to emulate characteristics of photovoltaic (PV) panels. Especially, the control of SAS is a challenging task due to the nonlinearity in the output curve, which is dependent on irradiance as well as temperature and is determined by panel materials. Conventionally, both current-mode control and voltage-mode control should be alternated by partitioning the operating curve into multiple sections, which is not only for the measurement noise problem with the feedback sensing but also for the control stability issue near the maximum power point. However, the occurrence of transition among different controllers may deteriorate the overall performance. To eliminate the mode transitions, a novel single controller scheme has been introduced in this paper, where the reference operating projection technique enables simple, smooth and numerically stable control. Theoretical consideration on the loop stability issue is discussed and the performance is verified experimentally for the emulation of a PV panel data in view of stability and response speed.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1868-1870
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• 2006

Neural network is used in many fields of control systems, but input-output patterns of a control system are not easy to be obtained and by using as single feedback neural network controller. And also it is difficult to get a satisfied performance when the changes of rapid load and disturbance are applied. To resolve those problems, this paper proposes a new algorithm which is the neural network controller. The new algorithm uses the neural network instead of activation function to control object at the output node. Therefore, control object is composed of neural network controller unifying activation function, and it supplies the error back propagation path to calculate the error at the output node. As a result, the input-output pattern problem of the controller which is resigned by the simple structure of neural network is solved, and real-time learning can be possible in general back propagation algorithm. Application of the new algorithm of neural network controller gives excellent performance for initial and tracking response and it shows the robust performance for rapid load change and disturbance. The proposed control algorithm is implemented on a high speed DSP, TMS320C32, for the speed of 3-phase induction motor. Enhanced performance is shown in the test of the speed control.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.310-312
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• 1994

A partial shunt regulator (PSR) which is the power regulator of KOREASAT is modeled. The modeling of the PSR consist of solar array, power circuit, controller. and load models. To realize simple structure. a voltage source of the PSR controller is used the output voltage of the PSR. The model of the PSR has very complex structure with two additional coupled feedback loops. The complex model is simplified to a simple meaningful model with only main feedback control loop. The proposed model is compared to a PSR model with DC voltage source at the PSR controller. The proposed PSR model is verified by comparing the model with SPICE simulation for small signal analysis.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1140-1146
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• 1999

In this paper, a force estimation method is proposed for force control without force sensor. For this , a disturbance observer is applied to each joint of an {{{{ { n}_{ } }}}} degrees of freedom manipulator to obtain a simple equivalent robot dynamics(SERD) being represented as an n independent double integrator system. To estimate the output of disturbance observer due to internal torque, the disturbance observer output estimator(DOOE) is designed, where uncertain parameters of the robot manipulator are adjusted by the gradient method to minimize the performance index which is defined as the quadratic form of the error signal between the output of disturbance observer and that of DOOE. when the external force is exerted, the external force is estimated by the difference between the output of disturbance observer and DOOE, since output of disturbance observer includes the external torque signal as well as the internal torque estimated by the output of DOOE. And then, a force controller is designed for force feedback control employing the estimated force signal. To verify the effectiveness of the proposed force estimation method, several numerical examples and experimental results are illustrated for the 2-axis direct drive robot manipulator.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.2
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• pp.149-157
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• 2001

A nonlinear speed control for a permanent magnet synchronous motor (PMSM) using a simple disturbance estimation technique is presented. By using a feedback linearization scheme, the nonlinear motor model can be linearized in a controllable canonical form, and the desired speed dynamics can be obtained based on the linearized model. This technique, however, gives an undesirable output performance under the mismatch of the system parameters and load conditions. To cancel disturbance by parameter variation, the controller parameters will be estimated by using a disturbance observer theory where the disturbance torque and flux linkage are estimated. since only the two reduced order observers are used for the parameter estimations, the observer designs are considerably simple and the additional load for computation of the controller is negligibly small. The proposed control scheme is implemented on a PMSM using DSP TMS320C31 and the effectiveness is verified through the comparative simulations and experiments.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.2
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• pp.31-37
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• 1982

A practical and robust control scheme is suggested for MIMO disciete time processes with real simple poles. This type of control scheme, having the advantages of both the adaptiveness and optimality, maybe successfully applicable to structured dynamic controllers for plants whose paiameters are slowly timevaiying. The identiflcation of the process paiameters is undertaken in ARMA form and the optimization of the feedback gain matrix is performed in the state space representation with respect to a standard quadratic criterion.

• Journal of Power Electronics
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• v.14 no.4
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• pp.712-721
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• 2014

This paper introduces a simple grid-voltage-sensorless control scheme for single-phase power factor correction (PFC) boost converters. The grid voltage waveform is obtained based on the dc output voltage, the switching duty ratio, and a phase-lead compensator. In addition, the duty ratio feedback is utilized to obtain the unity input power factor and the zero harmonic current. The proposed control scheme is designed and mathematically analyzed based on a small-signal model of PFC boost converters. To verify the effectiveness of the proposed control scheme, several simulations and experiments are carried out in two applications: an industrial power system with a 60 Hz grid frequency and a commercial aircraft application with a 400 Hz grid frequency.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.