• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.556-562
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• 2002

This paper studies the identification of a combustion model, which is used to design a linear controller of a steam generation quantity and harmful exhaust gases of a Refuse Incineration Plant(RIP). Even though the RIP has strong nonlinearities and complexities, it is identified as a MIMO parametric ARX model from experimental input-output data sets. Unknown model parameters are decided from experimental input-output data sets, using system identification algorithm based on Instrumental Variables(IV) method. It is shown that the identified model well approximates the input-output combustion characteristics.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.5
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• pp.17-23
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• 1985

This paper describes a method for the design of a self-tuning controller of single-input/single-output systems with system noises and obsrrvation noises. The method uses state-space techniques to assign the closed-loop system poles to desired locations, but the control law is made up of process input and output measurement values, so that state estimation is unnecessary. Also the difficulties of tracking of reference inputs in state.space pole-placing control are tackled by including the reference input in the cost function proposed by Beger.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.52-61
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• 1993

This paper deals with adaptive control method of a robot manipulator with one-flexible link. ARMA model is used as a prediction and estimation model, and adaptive control scheme consists of parameter estimation part and adaptive controller. Parameter estimation part estimates ARMA model's coefficients by using recursive least-squares(RLS) algorithm and generates the predicted output. Variable forgetting factor (VFF) is introduced to achieve an efficient estimation, and adaptive controller consists of reference model, error dynamics model and minimum prediction error controller. An optimal input is obtained by minimizing input torque, it's successive input change and the error between the predicted output and the reference output.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.237-242
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• 2000

The areal recording density of HDD(Hard Disk Drive) has been increasing by about 60% a year. In order to achieve high areal density, less track pitch is expected and more servo bandwidth is required. Dual stage actuator and servo controller for HDD have been suggested for achieving high track density as a possible solution. Dual-loop servo system is generally classified into a two-input-two-output system, but if we use an estimator for a two-input-two-output system, it can be converted into two input one output system. Since we can't control the dual stage servo system by the classical method, it requires a special technique; for example, Parallel Loop System, Master-Slave Loop System, Decoupled Master-Slave Loop System, and Dual Feedback Loop System. In this paper, we performed experimental evaluations of several types of control algorithm. Further experiments will be made in the future.

• Proceedings of the KIPE Conference
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• 1997.07a
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• pp.89-96
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• 1997

A robust speed control scheme for a brushless DC(BLDC) motor using an adaptive input-output linearization technique is presented. By using this technique, the nonlinear motor model can be linearized in Brunovski canonical form, and the desired speed dynamics can be obtained based on the linearized model. This control technique, however, gives an undesirable output performance under the mismatch of the system parameters and load conditions. For the robust output response, the controller parameters will be estimated by a model reference adaptive technique where the disturbance torque and flux linkage are estimated. The adaptation laws are derived by the Popov's hyperstability theory and positivity concept. The proposed control scheme is implemented on a BLDC motor using the software of DSP TMS320C30 and the effectiveness is verified through the comparative experiments.

• Journal of Electrical Engineering and information Science
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• v.2 no.3
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• pp.113-122
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• 1997

An adaptive input-output linearization technique for a robust speed control of a brushless C(BLDC) motor is presented. By using this technique, the nonlinear moro model can be effectively linearized in Brunovski canonical form, an the desired speed dynamics can be obtained based on the linearized model. This control technique, however, gives an undesirable output performance under the mismatch of the system parameters and load conditions caused by the incomplete linearization. for the robust output response, the controller parameters will be estimated by a model reference adaptive technique where the disturbance torque and flux linkage are estimated. The adaptation laws are derived by the Popov's hyperstability theory nd positivity concept. The proposed control scheme is implemented on a BLDC motor using the software of DSP TMS320C30 and the effectiveness is verified through the comparative simualtions and experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.548-552
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• 2005

In most physical processes, the transfer function includes a time-delay, and in the general distributed control system using a computer network, an inherent time-delay exists due to the spatial separation between controllers and actuators. Under the circumstance where an input time-delay exits, the system response overshoots and tends to diverge. For this reasons described above, a controller design method is proposed for a discrete nonlinear system including input time-delay, which adopts the time-discretization using Taylor series. Controllers are synthesized using an input/output linearization method. Finally, several cases of the computer simulations were conducted, and the results validate the proposed methods.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1142-1152
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• 2019

This paper presents a control method for reducing the current distortion in an indirect matrix converter (IMC) operating in boost mode under unbalanced input conditions. IMCs operating in boost mode are useful in distributed generation (DG) systems. They are connected with renewable energy systems (RESs) and the grid to transmit the power generated by the RES. However, under unbalanced voltage conditions of the RES, which is connected with the input stage of the IMC operating in boost mode, the input-output currents are distorted. In particular, the output current distortions cause a ripple of the power, which is transferred to the grid. This aggravates the reliability and stability of the DG system. Therefore, in this paper, a control method using positive/negative sequence voltages and currents is proposed for reducing the current distortion of both side in IMCs operating in boost mode. Simulation and experimental results have been presented to validate effectiveness of the proposed control method.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.327-330
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• 1996

It is well known that the controller output limits have a signifiant effect on the closed loop system performance. Considering the input constraints in GPCF, an effective selection method of the control weighting(.gamma.) is proposed to reduce the amplitude and the rate of control signals so that control signals lie within the limits. It is based on the relation between control weighting(.gamma.) and optimal solution of the unconstrained GPCF. The GPCFIC algorithm chooses an .gamma. at each sampling time so that all unconstrained GPCF output over the control horizon satisfy the rate and the amplitude constraints. In order to evaluate the performance of the GPCFIC, the computer simulations have been done for level control of PWR steam generator in low power operation and shown satisfactory results.

• Journal of Power System Engineering
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• v.7 no.1
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• pp.74-81
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• 2003

A system identification is to estimate the mathematical model on the base of input output data and to measure the output in the presence of adequate input for the controlled system. In the traditional system control field, most identification problems have been thought as estimating the unknown modeling parameters on the assumption that the model structures are fixed. In the system identification, it is possible to estimate the true parameter values by the adjusted least squares method in the input output case of no observed noise, and it is possible to estimate the true parameter values by the total least squares method in the input output case with the observed noise. We suggest the adjusted least squares method as a consistent estimation method in the system identification in the case where there is observed noise only in the output. In this paper the adjusted least squares method has been developed from the least squares method and the efficiency of the estimating results was confirmed by the generating data with the computer simulations.