• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2009년도 하계학술발표대회 논문집
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• pp.1025-1030
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• 2009

It is known that the classical tuning formula for typical PID controllers in general provides unsatisfactory results for industrial plants where the time delay exceeds the dominant lag time. For this reason, alternative strategies have been studied in order to cope with this problem and the most popular scheme is the Smith Predictor(SP). In this paper, the dynamic model of a unit apartment in the district heating system, which is the control process effected by the dead-time, is developed, and the on/off room temperature control method with the SP simulate using Matlab-Simulink. The simulation results show that the SP works effectively in outdoor temperature variation.

• 제어로봇시스템학회논문지
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• 제6권6호
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• pp.441-446
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• 2000

This paper presents an approach for the prediction of optimal heating load using a diagonal recurrent neural networks(DRNN) and data base system of outdoor temperature. In the DRNN, a dynamic backpropagation(DBP) with delta-bar-delta teaming method is used to train an optimal heating load identifier. And the data base system is utilized for outdoor temperature prediction. Compared to other kinds of methods, the proposed method gives better prediction performance of heating load. Also a hardware for the controller is developed using a microprocessor. The experimental results show that prediction enhancement for heating load can be achieved with the proposed method regardless of the its inherent nonlinearity and large time constant.