• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1557-1560
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• 1997

In this paper, we have built an experimental magnetic levitation system for a possible use of control education. We have give a mathermatical model of the nonlinear system and have shown the stability region of the linearized system when it is controlled by a PD controller. We also proposed a neural network control system which uses a neural network as a feedforward controller thgether with a conventional feedback PF controller. We have generated a desired output trajectory, which was designed for the benefit of the generalization of the neural network controller, and trained the desired output trajectory, which was desigend for the benefit of the generalization of the neural netowrk controller, and trained a neural network controller with the data of the actual input and the output of the system obtained by applying the desired output trajectroy. A good tracking performance was observed for both the desired trajectiories used and not used for the neural network training.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.406-409
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• 2000

We propose the modified neural network which are paralleled to control nonlinear systems. The proposed method is a direct control method to use inverse model of the plant. Nonlinear systems are divided into two parts; linear part and nonlinear part, and it is controlled by RLS method and recursive multi-layer neural network with each other. We simulate to verify the performance of the proposed method and are compared with conventional direct neural network control method. The proposed control method is improved the control performance than the conventional method.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.625-628
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• 1997

In this paper the new neural network and sliding mode suspension controller is proposed. That neural network is error self-recurrent neural network. For fast on-line learning, this paper use recursive least squares method. A new neural networks converges considerably faster than the backpropagation algorithm and has advantages of being less affected by the poor initial weights and learning rate. The controller for suspension systems is designed according to sliding mode technique based on new proposed neural network.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.861-865
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• 1991

In general, the fusion of neural network and fuzzy logic theory is based on the fact that neural network and fuzzy logic theory have the common properties that 1) the activation function of a neuron is similar to the membership function of fuzzy variable, and 2) the functions of summation and products of neural network are similar to the Max-Min operator of fuzzy logics. In this paper, a fuzzy-neural network will be proposed and a force controller of the robot gripper, utilizing the fuzzy-neural network, will be presented. The effectiveness of the proposed strategy will be demonstrated by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.195-195
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• 2000

In this paper, intelligent control method using neural network as a nonlinear controller is presented, Neural network controller is implemented on DSP board in PC to make real time computing possible, On-line training algorithm for neural network control is proposed, As a test-bed, a large a-x table was build and interface with PC has been implemented, Experimental results under different PD controller gains show excellent position tracking for circular trajectory compared with those for PD controller only.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.8-14
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• 2013

In this paper, a control system for two wheeled inverted pendulum robot is implemented to have more stable balancing capability than the conventional control system. Fuzzy control structure is chosen for the two wheeled inverted pendulum robot, and fuzzy membership function factors for the control system are obtained for 3 specified weights using a trial-and-error method. Next a neural network is employed to generate fuzzy membership function factors for more stable control performance when the weight is arbitrarily selected. Through some experiments, we find that the proposed fuzzy control system using the neural network is superior to the conventional fuzzy control system.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.102-108
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• 2018

The existing data transmission technology applied between the non-secure external internet and the secure internal business network has various problems when applied to the building facility management SCADA system control network. Traditional inter-network data transfer technologies involve high complexity and high costs because blacklist-based security techniques are applied to all data. However, whitelist-based security techniques can be applied to data distributed in Building Facility Management SCADA control systems because a small number of structured control data are repeatable and periodic. This simplifies the security technology applied to inter-network data transmission, enabling building facility management SCADA system control network deployment at low cost. In this paper, we proposed building control networks specialized in building facility management SCADA control systems by providing solutions to address and address these problems.

• Journal of Biomedical Engineering Research
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• v.19 no.1
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• pp.39-46
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• 1998

In this paper, we present a neural network identification and a control of highly complicated nonlinear left ventricular assist device(LVAD) system with a pneumatically driven mock circulation system. Generally, the LVAD system needs to compensate for nonlinearities. It is necessary to apply high performance control techniques. Fortunately, the neural network can be applied to control of a nonlinear dynamic system by learning capability. In this study, we identify the LVAD system with neural network identification(NNI). Once the NNI has learned the dynamic model of the LVAD system, the other network, called neural network controller(NNC), is designed for a control of the LVAD system. The ability and effectiveness of identifying and controlling the LVAD system using the proposed algorithm will be demonstrated by computer simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1167-1178
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• 1991

In this paper, we review the characteristics of battle field, consider required properties of mobile radio network, and design network control architecture proper to battle field network. Also we design distributed algorithm which constructs network control architecture and transmission schedule. For the information of network connectivity and the immediate reconfiguration caused by failure, loss, or movement of nodes, configuration and transmission schedule, algorithms are to be executed periodically and for entire network reliability, algorithms must to be executed fully distributedly. At the time of loss or failure of primary local control node, we suggest the method which diminishes transmission delay. We explain the operation of network with examples. Finally designed algorithms are verified and analized by computer simulation.

• The KIPS Transactions:PartC
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• v.11C no.4
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• pp.429-438
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• 2004

This Paper Presents the control structure for incoming traffic from arbitrary node to Provide admission control in policy-based W network management structure using fuzzy logic control approach. The proposed control structure uses scheme for deciding network resource allocation depending on requirements predefined-policies and network states. The proposed scheme enhances policy adapting methods of existing binary methods, and can use resource of network more effectively to provide adaptive admission control, according to the unpredictable network states for predefined QoS policies. Simulation results show that the proposed controller improves the ratio of packet rejection up to 26%, because it Performs the soft adaption based on the network states instead of accept/reject action in conventional CAC(Connection Admission Controller).