• Journal of Ocean Engineering and Technology
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• v.22 no.3
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• pp.34-40
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• 2008

his study discusses the design of an automatic berthing control algorithm for ships with a haw thruster and a stern thruster, as well as a rudder. A nonlinear mathematical model for the law speed maneuvering of ships was used to design a MIMO (multi-input multi-output) nonlinear control algorithm. The algorithm consists of two parts, the forward velocity control and heading angle control. The control algorithm was designed based on the longitudinal and yaw dynamic models of ships. The desired heading angle was obtained by the so-called "Line of Sight" method. An optimal control force allocation method forthe rudder and the thrusters is suggested. The nonlinear control algorithm was tested by numerical simulations using MATLAB, and showed good tracking performance.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.11
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• pp.394-403
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• 1983

This paper discusses a model reference adaptive control for a multi-input multi-output continuos system in matrix fraction description. The controller is of Monopoli-Narendra type with a time-varying gain matrix in the parameter adaptation law. The transfer matrix of the given plant with an adjustable controller is made to approach to that of the reference model asymptotically. It is shown that, under some plausible assumptions such as on the knowlidge of an interactor matrix, the algorithm for a single-input single-output system can be appropriately extended to a multi-input multi-output system. The convergence of an adaptation law is estavlished with some stability theory and stability of the overall system is asserted by an analytical investigation.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.269-277
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• 2008

This paper proposes a necessary and sufficient condition of convergence in the $L_2$-norm sense for a feedback-based iterative learning control (ILC) system including a multi-input multi-output (MIMO) linear time-invariant (LTI) plant. It is shown that the convergence conditions for a nominal plant and an uncertain plant are equal to the nominal performance condition and the robust performance condition in the feedback control theory, respectively. Moreover, no additional effort is required to design an iterative learning controller because the performance weighting matrix is used as an iterative learning controller. By proving that the least upper bound of the $L_2$-norm of the remaining tracking error is less than that of the initial tracking error, this paper shows that the iterative learning controller combined with the feedback controller is more effective to reduce the tracking error than only the feedback controller. The validity of the proposed method is verified through computer simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.950-960
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• 2010

In this paper, a multi-input multi-output(MIMO) integral variable structure system with an integral-augmented sliding surface is designed for the improved robust control of uncertain multivariable system under the matched persistent disturbance. To effectively remove the reaching phase problems, the integral augmented sliding surface is proposed. Then for its design, the eigenstructure assignment technique is introduced to. To guarantee the designed performance against the persistent disturbance, the stabilizing control for multi-input system is also designed to generate the sliding mode on the integral sliding surface. The stability of the global system together with the existence condition of the sliding mode are investigated and proved for the case of multi input system in the presence of uncertainty and disturbance. The reaching phase is completely removed in proposed MIMO VSS by satisfying the two requirements. An example and computer simulations will be present for showing the usefulness of algorithm.

• Bulletin of the Korean Chemical Society
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• v.24 no.3
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• pp.279-284
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• 2003

The One Pass Method (OPM) previously presented for the identification of single input single output systems is used to estimate the parameters of a Decentralized Control System (DCS). The OPM is a linear and therefore a simple estimation method. All of the calculations are performed in one pass, and no initial parameter guess, iteration, or powerful search methods are required. These features are of interest especially when the parameters of multi input-output model are estimated. The benefits of the OPM are revealed by comparing its results against those of two recently published methods based on pulse testing. The comparison is performed using two databases from the literature. These databases include single and multi input-output process transfer functions and relevant disturbances. The closed loop responses of these processes are roughly captured by the previous methods, whereas the OPM gives much more accurate results. If the parameters of a DCS are estimated, the OPM yields the same results in multi or single structure implementation. This is a novel feature, which indicates that the OPM is a convenient and practice method for the parameter estimation of multivariable DCSs.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.643-646
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• 1987

A criterion is developed for the, selection of the best pairing of the control and manipulated variables and for the interaction analysis of decentralized multi-input multi-output control systems. This criterion is based on the difficulty caused by the interaction terms in finding the in-verse of the block steady gain matrix. A quantitative measure of the best pairing is obtained from the resemblance of a set of independent block multi-loop systems. Several examples show the validity of the pairing criterion.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.3
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• pp.114-123
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• 1998

The multi-resonant converter(MRC) minimizes a parasitic oscillation by using the resonant tank circuit absorbed parasitic reactances existing in a converter circuit. So it si possible that the converter operated at a high frequency has a high efficiency because the losses are reduced. Such a MHz high frequency applications provide a high power density [W/inch3] of the converter. But the resonant voltage stress across a switch of the resonant tank circuit is 4~5 times a input voltage. This h호 voltage stress increases the conduction loss because of on-resistance of a MOSFET with higher rating. Thus, in this paper we proposed the alternated multi-resonant converter (AT MRC) differ from the clamp mode multi-resonant converter and applicated it to the forward MRC. The AT forward MRC can reduce the voltage stress to 2~3 times a input voltage by using two series input capacitor. The control circuit is simple because tow resonant switches are driven directly by the output pulse of the voltage controled oscillator. This circuit type is verified through the experimental converter with 48V input voltage, 5V/50W output voltage/power and PSpice simulation. the measured maximum voltage stress is 170V of 2.9 times the input voltage and the maximum efficiency of 81.66% is measured.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.663-671
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• 2008

This paper presents a real-time haptic rendering method for multi-contact interaction with virtual environments. Haptic systems often employ physics-based deformation models such as finite-element models and mass-spring models which demand heavy computational overhead. The haptic system can be designed to have two sampling times, T and JT, for the haptic loop and the graphic loop, respectively. A multi-rate output-estimation with an exponential forgetting factor is proposed to implement real-time haptic rendering for the haptic systems with two sampling rates. The computational burden of the output-estimation increases rapidly as the number of contact points increases. To reduce the computation of the estimation, the multi-rate output-estimation with reduced parameters is developed in this paper. Performance of the new output-estimation with reduced parameters is compared with the original output-estimation with full parameters and an exponential forgetting factor. Estimated outputs are computed from the estimated input-output model at a high rate, and trace the analytical outputs computed from the deformation model. The performance is demonstrated by simulation with a linear tensor-mass model.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.619-621
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• 2005

This paper proposes apractical design method for ship-to-ship missiles' autopilot. When the pre-designed analogue autopilot is implemented in digital way, theygenerally suffer from severe performance degradation and instability problem even for a sufficiently small sampling time. Also, aerodynamic uncertainties can affect the overall stability and this happens more severely when the nonlinear autopilot is digitally implemented. In order to realize a practical autopilot, two main issues, digital implementation problem and compensation for the aerodynamic uncertainties, are considered in this paper. MIMO (multi-input multi-output) nonlinear autopilot is presented first and the input and output of the missile are discretized for implementation. In this step, the discretization effect is compensated by designing an additional control input. Finally, we design a parameter adaptation law to compensate the control performance. Stability analysis and 6-DOF (degree-of-freedom) simulations are presented to verify the proposed adaptive autopilot.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.246-246
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• 2023

Multi-purpose dams are operated accounting for both physical and socioeconomic factors. This study aims to evaluate the utility of a deep learning algorithm-based model for three multi-purpose dam operation (Seomjin River dam, Juam dam, and Juam Control dam) in Seomjin River. In this study, the Gated Recurrent Unit (GRU) algorithm is applied to predict hourly water level of the dam reservoirs over 2002-2021. The hyper-parameters are optimized by the Bayesian optimization algorithm to enhance the prediction skill of the GRU model. The GRU models are set by the following cases: single dam input - single dam output (S-S), multi-dam input - single dam output (M-S), and multi-dam input - multi-dam output (M-M). Results show that the S-S cases with the local dam information have the highest accuracy above 0.8 of NSE. Results from the M-S and M-M model cases confirm that upstream dam information can bring important information for downstream dam operation prediction. The S-S models are simulated with altered outflows (-40% to +40%) to generate the simulated water level of the dam reservoir as alternative dam operational scenarios. The alternative S-S model simulations show physically inconsistent results, indicating that our deep learning algorithm-based model is not explainable for multi-purpose dam operation patterns. To better understand this limitation, we further analyze the relationship between observed water level and outflow of each dam. Results show that complexity in outflow-water level relationship causes the limited predictability of the GRU algorithm-based model. This study highlights the importance of socioeconomic factors from hidden multi-purpose dam operation processes on not only physical processes-based modeling but also aritificial intelligence modeling.