• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.11
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• pp.1035-1043
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• 2004

For a long road tunnel, a tunnel ventilation system may be used in order to reduce the pollution level below the required level. To control the tunnel pollution level, a closed loop control algorithm may be used. The feedforward prediction algorithm and the cascade control algorithm were developed to regulate the CO level in a tunnel. The feedforward prediction algorithm composed of the traffic estimation algorithm and the CO density prediction algorithm, and the cascade control algorithm composed of the jet fan control algorithm and the air velocity setpoint algorithm. The verification of control algorithms was carried out by dynamic models developed from the actual tunnel data. The simulation results showed that control algorithms developed for this study were effective for the control of the tunnel ventilation system.

• Proceedings of the SAREK Conference
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• 2008.11a
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• pp.399-404
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• 2008

Condensing gas boiler units may make a big role for the reduction of energy consumption in heating industries. In order to decrease the energy consumption of a condensing gas boiler unit, the effective control of the system is necessary. In this study, control algorithms of a condensing gas boiler were developed. Control algorithms are composed of the setpoint algorithm and the control algorithm. The setpoint algorithm consists of the supply water temperature setpoint algorithm and the pump setpoint algorithm. The control algorithm consists of the gas valve control algorithm and the blower control algorithm. In order to analyse the performance of control algorithms, dynamic models of a condensing gas boiler system were used. Simulation results showed that control algorithms developed for this study may be practically applied to the condensing gas boiler.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.9
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• pp.2153-2161
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• 1993

Self-tuning control algorithms for an input-amplitude constrained system are developed and implemented. Magnitude of control input for small motors is generally restricted to narrow bound due to actuator saturation. The gain-adjusted control algorithm and the bounded-gain control algorithm proposed in this study yield smoother control input variations within the magnitude constraints comparing with the existing Clarke's suboptimal control algorithm. In the gain-adjusted control algorithm, the feedforward gain is adjusted using maximum gain, while in the bounded-gain control algorithm, the feedforward gain is bounded using weighting factor. For the DC servo motor control, the system performances of the proposed algorithms are compared with those of the existing algorithm by computer simulation and experiment. It is shown that the input variations of the proposed algorithms are smoother as compared with the existing algorithm.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.435-440
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• 2009

For the effective control of a $CO_2$ air-conditioning system, the system high-side pressure algorithm, the indoor temperature algorithm, and the outdoor fan algorithm were developed. The system high-side pressure algorithm was composed of the setpoint algorithm, the reset algorithm, and the electronic expansion valve control algorithm. The indoor temperature algorithm was composed of the compressor control algorithm and the indoor fan control algorithm. These algorithms were tested by using mathematical models developed from the previous study. Results from the setpoint step change test and the disturbance test showed good control performances. Therefore, algorithms developed in this study may practically used for the control of a $CO_2$ air-conditioning system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.737-742
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• 2007

The method of the reducing duct noise can be classified by passive and active control techniques. However, passive control has a limited effect of noise reduction at low frequencies (below 500Hz) and is limited by the space. On the other hand, active control can overcome these passive control limitations. The active control technique mostly uses the Least-Mean-Square (LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time particularly when the Filtered-X LMS (FXLMS) algorithm is applied to an active noise control (ANC) system. However, the convergence performance of the LMS algorithm decreases slightly so it may delay the convergence time when the FXLMS algorithm is applied to the active control of duct noise. Thus the Co-FXLMS algorithm was developed to improve the control performance in order to solve this problem. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Co-FXLMS algorithm is presented in comparison with the FXLMS algorithm. Simulation results show that active noise control using Co-FXLMS is effective in reducing duct noise.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.2
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• pp.147-155
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• 2005

For a long road tunnel, a tunnel ventilation system may be used in order to reduce the pollution level below the required level. To control the tunnel pollution level, a closed loop control algorithm may be used. The cascade control algorithm, which composed of a jet fan control algorithm and an air velocity setpoint algorithm, was developed to regulate the CO level in a tunnel. The verification of control algorithms was carried out by dynamic models developed from real tunnel data sets. The simulation results showed that control algorithms developed for this study were effective to control the tunnel ventilation system.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.4
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• pp.498-504
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• 1998

This paper presents an optimal control algorithm for the overhead crane. To control the swing motion and the position tracking of the payload of the overhead crane a state feedback control algorithm is applied. by using a hybrid genetic algorithm the feedback gains of the state feedback is optimized to minimize the cost function composed of position errors and payload swing angle under unknown constant disturbances. Computer simulation is performed to demonstrate the effectiveness of the proposed control algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.297-300
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• 1997

This paper uses genetic algorithm (GA) for optimal control. GA can find optimal control profile, but the profile may be oscillating feature. To make profile smooth, fuzzy genetic algorithm (FGA) is proposed. GA with fuzzy logic techniques for optimal control can make optimal control profile smooth. We describe the Fuzzy Genetic Algorithm that uses a fuzzy knowledge based system to control GA search. Result from the simulation example shows that GA can find optimal control profile and FGA makes a performance improvement over a simple GA.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.18 no.2
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• pp.163-171
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• 2006

In order to control multi-zone temperatures, a multi-type air-conditioning system may be used. In this study, control algorithms for the compressor and the electronic expansion valve of a multi-type air-conditioning system were developed by using fuzzy logics. The compressor control algorithm was composed of a compressor pressure setpoint algorithm, a compressor pressure setpoint reset algorithm, and a compressor frequency setpoint algorithm. The electronic expansion valve control algorithm was composed of an indoor temperature control algorithm, and a superheat control algorithm. These algorithms were applied to a multi-type air-conditioning system. Test showed good results for the control of a multi-type air-conditioning system.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.85.1-85
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• 2002

In this paper, a control strategy of position and force is proposed based on a switching algorithm. The main focus is the control of position and force in the same direction. The switching algorithm based on a fuzzy algorithm determines the weighting value of force control. First, the force control is dominant. If the position gets closer to the desire position, the weighting value of force control is closer to zero. The proposed algorithm is shown to be satisfactory to position and force control and the weighting factor is quite successful by simulation examples.