• Journal of Institute of Control, Robotics and Systems
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• v.10 no.11
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• pp.1071-1076
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• 2004

It is a fuzzy controller that it is the most used method in the control of non-linear system. The most important part in the fuzzy controller is a design of fuzzy rules. Many algorithm that design fuzzy rules have proposed. And attention to the evolutionary computation is increasing in the recent days. Among them, the co-evolutionary algorithm is used in the design of optimal fuzzy rule. This paper takes advantage of a schema co-evolutionary algorithm. In order to verify the efficiency of the schema co-evolutionary algorithm, a fuzzy controller for the mobile robot control is designed by the schema co-evolutionary algorithm and it is compared with other parasitic co-evolutionary algorithm such as a virus-evolutionary genetic algorithm and a co-evolutionary method of Handa.

• 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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.3
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• pp.284-292
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• 2008

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, FXLMS algorithm has the demerit that stability of the control is decreased when the step size become larger but the convergence speed is faster because the step size of FXLMS algorithm is fixed. As a result, the system has higher probability which the divergence occurs. Thus the Co-FXLMS algorithm was developed 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 and experimental results show that active noise control using Co-FXLMS is effective in reducing the noise in duct system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.598-603
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• 2007

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, FXLMS algorithm has the demerit that stability of the control is decreased when the step size become larger but the convergence speed is faster because the step size of FXLMS algorithm is fixed. As a result, the system has higher probability which the divergence occurs. Thus the Co-FXLMS algorithm was developed 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 the noise in duct system.

• 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 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 Bio-Environment Control
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• v.2 no.1
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• pp.27-36
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• 1993

Carbon dioxide enrichment for greenhouse crops has generally been a standard commercial practice for many years. Vegetable crops such as tomato, cucumber, and lettuce respond positively to the $CO_2$ enrichment. But improper $CO_2$ enrichment leads to physiological damage and economical loss. This study was carried out to develop a $CO_2$ concentration control algorithm considering growth stage and efficiency. The measurand was $CO_2$ consumption rate and top fresh weight that represents growth stage. The weight of top fresh lettuce as a whole in the tray was measured through a non-destructive method. The demand in $CO_2$ concentration according to growth stage was investigated. The results are summarized as follows. 1. The $CO_2$ consumption rate could be measured within the error of $\pm$ 15.4mg$CO_2$/hr in the range of $CO_2$ concentration of 500-1500ppm. 2. The weight of top fresh lettuce could be measured within the error $\pm$ 4.3g in the range of 0-1400g. 3. The $CO_2$ control model developed could determine an economical $CO_2$ supply rate considering $CO_2$ consumption rate and leakage rate. 4. The $CO_2$ control algorithm based on the control model was composed of feedforward control for maintaining a stable $CO_2$ concentration level, and feedback control with $CO_2$ consumption rate and top fresh weight for adapting to the change in $CO_2$ demand by growth stage. 5. For the performance test with the developed control algorithm on lettuce the decrease in $CO_2$ supply rate was obtained without a significant decrease in top fresh weight.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.10
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• pp.1489-1495
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• 2012

In this study, the active noise control experiment has been performed using induction motor noises. While the noises were measured, a induction motor was operated in different speed. For the simulation of ANC(Active Noise Control), test-bed is composed a multi-channel ANC system was constructed. In order to compare the control performance, we performed noise reduction simulations of ANC by Co-FXLMS algorithm and FXLMS algorithm. Through the simulation results, we confirmed that convergence performance and noise decrease effect of the proposed Co-FXLMS algorithm have been improved from existing FXLMS algorithm.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.403-410
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• 2005

Nowadays, versatile robots are developed around the world. Novel algorithms are needed for controlling such robots. A 2-Layer fuzzy controller can deal with many inputs as well as many outputs, and its overall structure is much simpler than that of a general fuzzy controller. The main problem encountered in fuzzy control is the design of the fuzzy controller. In this paper, the fuzzy controller is designed by the schema co-evolutionary algorithm. This algorithm can quickly and easily find a global solution. Therefore, the schema co-evolutionary algorithm is used to design a 2-layer fuzzy controller in this study. We apply it to a mobile robot and verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm through the experiments.