• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.421-424
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• 2008

The real time modeling of dynamic system on adaptive control system is very important for flying control system(FCS). Using traditional method, it is required much calculation load for integral/differential at control system. Therefore, It is very important theme of study in these days to find algorithms for integration/differential at FCS. These algorithms for integral/differential influence strongly stability/reliability to control flying object. In this paper, we present optimal predictive sampling time for reduce calculation load at FCS and optimal predictive time on general cost function by applying adaptive control method.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.2043-2050
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• 2017

This paper presents two MPC (Model Predictive Control) based charging and discharging algorithms of BESS (Battery Energy Storage System) for capacity firming of wind generation. To deal with the intermittency of the output of wind generation, a single BESS is employed. The proposed algorithms not only make the output of combined systems of wind generation and BESS track the predefined reference, but also keep the SoC (State of Charge) of BESS within its physical limitation. Since the proposed algorithms are both presented in simple if-then statements which are the optimal solutions of related optimization problems, they are both easy to implement in a real-time system. Finally, simulations of the two strategies are done using a realistic wind farm library and a BESS model. The results on both simulations show that the proposed algorithms effectively achieve capacity firming while fulfilling all physical constraints.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.621-623
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• 2000

In this paper, the effective design method of the predictive controller using fuzzy neural networks(FNNs) is presented for the Intelligent control of chaotic nonlinear systems. In our design method of controller, predictor parameters are tuned by the error value between the actual output of a chaotic nonlinear system and that of a fuzzy neural network model. And the parameters of predictive controller using fuzzy neural network are tuned by the gradient descent method which uses control error value between the actual output of a chaotic nonlinear system and the reference signal. In order to evaluate the performance of our controller, it is applied to the Duffing system which are the representative continuous-time chaotic nonlinear systems and the Henon system which are representative discrete-time chaotic nonlinear systems.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.13 no.6
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• pp.284-289
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• 2003

The electric furnace, inside which the desired temperature is kept by the generated heat, is known to be a difficult system to control and model exactly because system parameters and response delayed time are varied as the temperature and positions are changed. In this study, the GPCEW (generalized predictive control with exponential weight), which always guarantees the stability of the closed loop system and can be effectively applied to the internally unstable system, was introduced to the ceramic drying electric furnace and was verified by showing its temperature tracking performance experimentally.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.8
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• pp.557-562
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• 1988

This paper presents a class of discrete adaptive controller that can be applied to a plant without sufficient a priori information. It is well known that the GMV(Generalized Minmum Variance) contrlller performs satisfactorily if the plant time delay is known. By introducing the long-range prediction into the GMV controller, robustness to the time delay can be improved, although optimality is lost. Such an idea motivates a predictive control system to be proposed here, where the system minimizes multi-stage cost via the GMV approach. Moreover, the detuning control weight is determined by an on-line tuning method. It is shown that robustness, computational efficiency, and performance of the resulting control system are improved as compared with those of the GPC(Generalized Predictive Control)system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6B
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• pp.641-647
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• 2009

This paper proposes an improved predictive dynamic power management (DPM) scheme and a task scheduling algorithm to reduce unnecessary power consumption in embedded systems. The proposed algorithm performs pre-scheduling to minimize unnecessary power consumption. The proposed predictive DPM utilizes a scheduling library provided by the system to reduce computation overhead. Experimental results show that the proposed algorithm can reduce power consumption by 22.3% on the average comparing with the LLF algorithm for DPM-enable system scheduling.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.1
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• pp.3-11
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• 2007

THM models have been developed in several researchers in order to better understand and manage the presence of THM in water distribution system. Several developed models were demonstrated in this study for estimating THM concentrations in target water distribution system. In order to investigate the performance of developed THM models, lab and field test were investigated. Predicted THM concentrations by all kind of models were showed good correlation with observed values. When the developed models were compared with lab and field test, the Rodriguez model during tested models was most predictive than the other models.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.123-130
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• 2024

This paper proposes an EMPC (Explicit Model Predictive Controller) for temperature tracking control based on heat load prediction by an ESO (Extended State Observer) for a variable cooling circulation system with multiple indoor units connected to one outdoor unit. In this system, heat transfer and heat loss relative to the input temperature are modeled using system dynamics. Using this model, we design an EMPC based on an ESO that is robust to temperature changes and depends on airflow. To determine the stability of both the controller and the observer, asymptotic stability is verified through Lyapunov stability analysis. Finally, to validate the performance of the proposed controller, simulations are conducted under three scenarios with varying airflow, set temperature, and heat load.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.165-175
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• 2015

Energy storage system has been widely applied in power distribution sectors as well as in renewable energy sources to ensure uninterruptible power supply. This paper presents a model predictive algorithm to control a bidirectional AC-DC converter, which is used in an energy storage system for power transferring between the three-phase AC voltage supply and energy storage devices. This model predictive control (MPC) algorithm utilizes the discrete behavior of the converter and predicts the future variables of the system by defining cost functions for all possible switching states. Subsequently, the switching state that corresponds to the minimum cost function is selected for the next sampling period for firing the switches of the AC-DC converter. The proposed model predictive control scheme of the AC-DC converter allows bidirectional power flow with instantaneous mode change capability and fast dynamic response. The performance of the MPC controlled bidirectional AC-DC converter is simulated with MATLAB/Simulink(R) and further verified with 3.0kW experimental prototypes. Both the simulation and experimental results show that, the AC-DC converter is operated with unity power factor, acceptable THD (3.3% during rectifier mode and 3.5% during inverter mode) level of AC current and very low DC voltage ripple. Moreover, an efficiency comparison is performed between the proposed MPC and conventional VOC-based PWM controller of the bidirectional AC-DC converter which ensures the effectiveness of MPC controller.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.397-407
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• 2012

This study aims to develop the methods for effective managing national health screening patients in the health examination center using digital data from national health screening in Dae-Jeon health examination center. To achieve this, we collected about national health screening for 10 years from 2002 to 2011 in Dae-Jeon health examination center and developed re-examination predictive model, private examination predictive model and stomach cancer examination predictive model for national health screening patients by using this data. According to the predictive model results, age, residence, group or individual health examination and the previous number of national health screening were statistically associated with re-examination, private examination, stomach cancer examination. We came up with a plan for health examination center system based on the predictive model and logic in Dae-Jeon. Customized service based on patient management system for national health screening will contribute to efficiency in health examination center.