• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.43.6-43
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• 2001

In this paper, we describe an actor-critic method as a kind of temporal difference (TD) algorithms. The value function is regarded as a current estimator, in which two value functions have different inputs: one is an actual experience; the other is a simulated experience obtained through a predictive model. Thus, the parameter´s updating for the actor and critic parts is based on actual and simulated experiences, where the critic is constructed by a radial-basis function neural network (RBFNN) and the actor is composed of a kinematic-based controller. As an example application of the present method, a tracking control problem for the position coordinates and azimuth of a nonholonomic mobile robot is considered. The effectiveness is illustrated by a simulation.

• Journal of the Korean Institute of Gas
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• v.15 no.1
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• pp.1-8
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• 2011

There has been much research interest in developing processes for production and utilization of gas and novel renewable energy resources. For these process to be economically viable, implementation of a suitable process control technique is required. This paper reviews some of the major process control techniques that have been developed over the last 50 years. In addition, some control applications in gas process industries are also presented with future directions.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.8
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• pp.627-635
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• 2012

This study aims at developing an artificial neural network(ANN)-based predictive and adaptive temperature control method to control the openings at internal and external skins, and heating systems used in a building with double skin envelope. Based on the predicted indoor temperature, the control logic determined opening conditions of air inlets and outlets, and the operation of the heating systems. The optimization process of the initial ANN model was conducted to determine the optimal structure and learning methods followed by the performance tests by the comparison with the actual data measured from the existing double skin envelope. The analysis proved the prediction accuracy and the adaptability of the ANN model in terms of Root Mean Square and Mean Square Errors. The analysis results implied that the proposed ANN-based temperature control logic had potentials to be applied for the temperature control in the double skin envelope buildings.

• KIEAE Journal
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• v.14 no.3
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• pp.97-103
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• 2014

This study aimed at developing integrated logic for controlling heating device and openings of the double skin facade buildings. Two major logics were developed-rule-based control logic and artificial neural network based control logic. The rule based logic represented the widely applied conventional method while the artificial neural network based logic meant the optimal method. Applying the optimal method, the predictive and adaptive controls were feasible for supplying the advanced thermal indoor environment. Comparative performance tests were conducted using the numerical computer simulation tools such as MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation). Analysis on the test results in the test module revealed that the artificial neural network-based control logics provided more comfortable and stable temperature conditions based on the optimal control of the heating device and opening conditions of the double skin facades. However, the amount of heat supply to the indoor space by the optimal method was increased for the better thermal conditioning. The number of on/off moments of the heating device, on the other hand, was significantly reduced. Therefore, the optimal logic is expected to beneficial to create more comfortable thermal environment and to potentially prevent system degradation.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1265-1274
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• 2018

This paper proposes a virtual flux (VF) and positive-sequence power based control strategy to improve the performance of grid-interfaced three-phase voltage source converters against unbalanced and distorted grid conditions. By using a second-order generalized integrator (SOGI) based VF observer, the proposed strategy achieves an AC voltage sensorless and grid frequency adaptive control. Aiming to realize a balanced sinusoidal line current operation, the fundamental positive-sequence component based instantaneous power is utilized as the control variable. Moreover, the fundamental negative-sequence VF feedforward and the harmonic attenuation ability of a sequence component generator are employed to further enhance the unbalance regulation ability and the harmonic tolerance of line currents, respectively. Finally, the proposed scheme is completed by combining the foregoing two elements with a predictive direct power control (PDPC). In order to verify the feasibility and validity of the proposed SOGI-VFPDPC, the scenarios of unbalanced voltage dip, higher harmonic distortion and grid frequency deviation are investigated in simulation and experimental studies. The corresponding results demonstrate that the proposed strategy ensures a balanced sinusoidal line current operation with excellent steady-state and transient behaviors under general grid conditions.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.2
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• pp.23-30
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• 2012

This paper proposes a novel adaptive header-based power gating structure to compensate for the performance loss and the increased wake-up time of the power gating structures induced by the negative bias temperature instability (NBTI) effect. The proposed structure consists of variable width footers based on the two-pass power gating and a new NBTI sensing circuit for an adaptive control. The simulation results of the proposed structure are compared to those of power gating without the adaptive control and show that both the circuit-delay and wake-up time dependence of the power gating structure on the NBTI stress is minimized with only 3% and 4% increase, respectively while keeping small leakage power and rush-current. In this paper, a 45 nm CMOS technology and predictive NBTI model have been used to implement the proposed circuits.

• KIEAE Journal
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• v.14 no.3
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• pp.71-77
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• 2014

This study aimed at developing and evaluating performance of the two logics for respectively operating two-position- and variable-heating systems. Both logics control the heating system and openings of the double skin facade buildings in an integrated manner. Artificial neural network models were applied for the predictive and adaptive controls in order to optimally condition the indoor thermal environment. Numerical computer simulation methods using the MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation) were employed for the performance tests of the logics in the test module. Analysis on the test results revealed that the variable control logic provided more comfortable and stable temperature conditions with the increased comfortable period and the decreased standard deviation from the center of the comfortable range. In addition, the amount of heat supply to the indoor space was significantly reduced by the variable control logic. Thus, it can be concluded that the optimal control method using the artificial neural network model can work more effectively when it is applied to the variable heating systems.

• Proceedings of the SAREK Conference
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• 2008.11a
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• pp.405-409
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• 2008

Over 70% of the land is mountains in Korea, so that many roadways naturally includes tunnels. The air flow inside tunnel has complex characteristics, such that a new flow field is formed by following vehicles passing through the tunnel before previous flow field is stabilized. Due to these time delayed-transient characteristics, the ventilation facility requires the complex control algorithm that can handle adaptive and predictive controls. Also, it needs to be closely related to the disaster prevention system. The technology to integrate these system determines the success of TGMS. The pollutant levels exhausted from the vehicles passing through tunnel depend on vehicle years and passing velocity. They also depend on the slope and altitude of the tunnel. In order to solve this problem, an algorithm for estimating the compensating factors for calculating on design capacity of ventilation facilities was developed. Also, an integrated ventilation control algorithm with disaster prevention program to operate several tunnels was developed based on TGMS.

• Proceedings of the KIPE Conference
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• 2004.07b
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• pp.575-578
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• 2004

The performance of an active power filter(APF) depends on the inverter characteristic, the control method, and the accuracy of reference signal generator. The accuracy of reference generator is the most critical item to determine the performance of active power filter. This paper introduces a novel reference signal generator composed of improved adaptive predictive filter. The performance of proposed reference signal generator was first verified through a simulation with MATLAB. Furthermore, the application of feasibility was evaluated through experimenting with a single-phase APF prototype based on the proposed reference generator, which was implemented using the TMS320C31 floating-point signal processor. Both simulations and experimental results confirm that our reference signal generator can be used successfully in practical active power filters.

• Journal of Computing Science and Engineering
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• v.10 no.3
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• pp.75-84
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• 2016

Medical cyber-physical systems (MCPS) integrate sensors, actuators, and software to improve patient safety and quality of healthcare. These systems introduce major challenges to safety analysis because the patient's physiology is complex, nonlinear, unobservable, and uncertain. To cope with the challenge that unidentified physiological parameters may exhibit short-term variances in certain clinical scenarios, we propose a novel run-time predictive safety monitoring technique that leverages a maximal model coupled with online training of a computational virtual subject (CVS) set. The proposed monitor predicts safety-critical events at run-time using only clinically available measurements. We apply the technique to a surgical glucose control case study. Evaluation on retrospective real clinical data shows that the algorithm achieves 96% sensitivity with a low average false alarm rate of 0.5 false alarm per surgery.