• Journal of Advanced Marine Engineering and Technology
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• v.26 no.4
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• pp.415-424
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• 2002

In this paper, a robust fuzzy precompensated PID controller using immune algorithm for load frequency control of 2-area power system is proposed. Here, a fuzzy precompensated PID controller is designed as a fuzzy logic based precompensation approach for PID controller. This scheme is easily implemented by adding a fuzzy precompensator to an existing PID controller. We optimize the fuzzy precompensator with an immune algorithm for complementing the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership function and fuzzy rules. Simulation results show that the proposed robust load frequency controller can achieve good performance even in the presence of generation rate constraints.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.8
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• pp.1482-1488
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• 2010

Recently to increase the productivity and improve the quality in the industrial process, suppressing the residual vibration in motion control systems becomes the essential problem to solve. One of the methods to suppress the residual vibration is the input shaping technique. It is based on parameters of the system model; however, the parameters are usually difficult to obtain. This paper shows the effects of the residual vibration caused by the variation of the general velocity profile for the system with two vibration modes, and also shows the effects of the input shaping filter based on the parameters of system model. Finally, the simulation results show that the proposed input shaping filter using an artificial immune algorithm is more effective for suppressing residual vibrations than genetic algorithm.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.11
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• pp.1103-1109
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• 2009

In this paper, the control of a membrane used in reverse osmosis desalination plant by using immune algorithm(IA) is addressed. The proposed algorithm IA of auto tuning method can find optimal gains and compared with conventional Ziegler-Nichols tuning method. The results of computer simulation represent that the proposed IA shows a good control performances better than Ziegler-Nichols tuning method.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.561-567
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• 2006

This paper deals with a fuzzy controller using IA(Immune Algorithm) for Trajectory Tracking of 2-DOF WMR(Wheeled Mobile Robot). The global inputs to the WMR are reference position and reference velocity, which are time variables. The global output of WMR is a current position. The tracking controller makes position error to be converged 0. In order to reduce position error, a compensation velocities on the track of trajectory is necessary. Therefore, a FIAC(Fuzzy-IA controller) is proposed to give velocity compensation in this system. Input variables of fuzzy part are position errors in every sampling time. The output values of fuzzy part are compensation velocities. IA are implemented to adjust the scaling factor of fuzzy part. The computer simulation is performed to get the result of trajectory tracking and to prove efficiency of proposed controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.113-117
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• 2002

PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But it is difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose a nonlinear adaptive PR controller based on an Immune feedback mechanism and a gradient descent teaming. This algorithm has a simple structure and robustness to system parameters variation. To verify performances of the proposed nonlinear adaptive PID controller, the speed control of nonlinear DC motor Is peformed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2573-2575
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• 2003

In this paper, we discuss a fuzzy neural network classifier with immune algorithm. The fuzzy neural network classifier is constructed with the fuzzy classifier and the neural network classifier based on fuzzy rules. To maximize performance of classifier, the immune algorithm and the back propagation algorithm are used. For the generalized classification ability, the simulation results from the iris data demonstrate superiority of the proposed classifier in comparison with other classifier.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.429-435
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• 2006

In this study multi-zone modeling program CONTAM 2.4 which is developed by NIST is used for modeling the air disinfection system which is consist of dilution, filtration, ultra violet germicidal irradiation (UVGI) for removing the indoor microorganism such as bacteria and fungus. Developed models those protect occupants against indoor microorganism generated in our daily life are enable to use for immune building simulation tool. Also, results indicate that those models are enable to compute the real situation that is almost impossible of carrying out experiment and identify the disinfection rate with highly reliance. Results also suggest that engineers will use these models as a design tool for the immune building system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.257-260
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• 2007

In this paper addresses the low-level behavior of fuzzy control and the high-level behavior selector for Autonomous Mobile Robots (AMRs) based on a Fuzzy Artificial Immune Network. The sensing information that comes from ultrasonic sensors is the antigen it, and stimulates antibodies. There are many possible combinations of actions between action-patterns and external situations. The question is how to handle the situations to decide the proper action. We propose a fuzzy artificial immune network to solve the above problem. and the computer simulation for an AMR action selector shows the usefulness of the proposed action selector.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2083-2089
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• 2007

In this paper addresses the low-level behavior of fuzzy control and the high-level behavior selector for Autonomous Mobile Robots(AMRs) based on a Fuzzy Artificial Immune Network. The sensing information that comes from ultrasonic sensors is the antigen it, and stimulates antibodies. There are many possible combinations of actions between action-patterns and external situations. The question is how to handle the situations to decide the proper action. We propose a fuzzy artificial immune network to solve the above problem. and the computer simulation for an AMR action selector shows the usefulness of the proposed action selector.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3B
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• pp.311-317
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• 2009

The traditional network anomaly detection systems execute the threshold-based detection without considering dynamic network environments, which causes false positive and limits an effective resource utilization. To overcome the drawbacks, we present the adaptive network anomaly detection model based on artificial immune system (AIS) in centralized network. AIS is inspired from human immune system that has learning, adaptation and memory. In our proposed model, the interaction between dendritic cell and T-cell of human immune system is adopted. We design the main components, such as central node and router node, and define functions of them. The central node analyzes the anomaly information received from the related router nodes, decides response policy and sends the policy to corresponding nodes. The router node consists of detector module and responder module. The detector module perceives the anomaly depending on learning data and the responder module settles the anomaly according to the policy received from central node. Finally we evaluate the possibility of the proposed detection model through simulation.