• Journal of Ocean Engineering and Technology
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• v.16 no.1
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• pp.8-15
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• 2002

This paper presents a neural net based nonlinear adaptive controller for an autonomous underwater vehicle (AUV). AUV's dynamics are highly nonlinear and their hydrodynamic coefficients vary with different operational conditions, so it is necessary for the high performance control system of an AUV to have the capacities of learning and adapting to the change of the AUV's dynamics. In this paper a linearly parameterized neural network is used to approximate the uncertainties of the AUV's dynamic, and the basis function vector of network is constructed according to th AUV's physical properties. A sliding mode control scheme is introduced to attenuate the effect of the neural network's reconstruction errors and the disturbances in AUV's dynamics. Using Lyapunov theory, the stability of the presented control system is guaranteed as well as the uniformly boundedness of tracking errors and neural network's weights estimation errors. Finally, numerical simulations for motion control of an AUV are performed to illustrate the effectiveness of the proposed techniques.

• The KIPS Transactions:PartC
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• v.8C no.5
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• pp.573-584
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• 2001

Policy-Based Network Management (PBNM) architecture is to meet various needs of network users and to provide effective management facilities in distributed and large scale networks to network managers. In PBNM, network managers perform network management operations by stipulating a set of rules rather than control each network component. On the other hand, providing security services such as authentication, privacy of messages as well as a new flexible and extensible administration framework, SNMPv3 enables network managers to monitor and control the operation of network components more secure way than ever before. Despite of its enhanced security services, SNMPv3 has difficulties in managing distributed, large-scaled network because it does not provide centralized security management facilities. In this paper, we propose a new security model called Role-based Security Management model (RSM) with security management policy to support scalable and centralized security management for SNMP-based networks. Also, the structure and the operation of the security system as well as the efficiency analysis of RSM in terms of security management are also described.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.472-474
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• 1998

We study the transient stability control problem for electric power system. Passivity-based control method is used to obtain transient stability. We propose a method which uses neural network to obtain passivity and the transient stability, and compare the simulation result with that of speed gradient method which was developed by other researchers.

• Journal of Mechanical Science and Technology
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• v.18 no.4
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• pp.535-549
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• 2004

This paper proposes a sliding mode controller with fuzzy adaptive perturbation compensator(FAPC) to get a good control performance and reduce the chatter, The proposed algorithm can reduce the chattering because the proposed fuzzy adaptive perturbation compensator compensates the perturbation terms. The compensator computes the control input for compensating unmodeled dynamic terms and disturbance by using the observer-based fuzzy adaptive network(FAN) The weighting parameters of the compensate. are updated by on-line adaptive scheme in order to minimize the estimation error and the estimation velocity error of each actuator. Therefore, the combination of sliding mode control and fuzzy adaptive network gives the robust and intelligent routine to get a good control performance. To evaluate the control performance of the proposed approach, tracking control is experimentally carried out for the hydraulic motion platform which consists of a 6-DOF parallel manipulator.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1103-1111
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• 1999

STACOM will be utilized to control substation voltage in the near future. Although STACOM shows good voltage regulation performance owing to its rapid and continuous response, it needs additional reactive power compensation device to keep control margin for emergency such as fault. ULTC transformer is one of good candidates. This paper presents a Kohonen Neural Network (KNN) based coordination control scheme of ULTC transformer and STACOM. In this paper, the objective function of the coordination control is minimization of both STACOM output and the number of switchings of ULTC transformer while maintaining substation voltage magnitude to the predefined constant value. This coordination, control is performed based on reactive load trend of the substation and KNN which offers optimal tap position in view of STACOM output minimization. The input variables of KNN are active and reactive power of the substation, current tap position, and current STACOM output. The KNN is trained by effective Iterative Condensed Nearest Neighbor (ICNN) rule. This coordination control applied to IEEE 14 bus system and shows satisfactory results.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.118.2-118
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• 2002

Uncertain time delay happens when the process reads the sensor data and sends the control input to the plant located at a remote site in distributed control system. As in the case of data network using TCP/IP, VDN that integrates both device network and data network has uncertain tim e delay. Uncertain time delay can cause degradation in stability of distributed control system based on VDN. This paper investigates the transmission characteristic of VDN and suggests a control scheme based on the Smith's predictor to minimize the effect of uncertain time delay. The validity of the proposed control scheme is demonstrated with tracking position control of experiments.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.897-900
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• 1998

Through fuzzy logic controller is very useful to many areas, it is difficult to build up the rule-base by experience and trial-error. So, effective self-tuning fuzzy controller for the position control of ball and beam is designed. In this paper, we developed the neural network control system with fuzzy-neuron which conducts the adjustment process for the parameters to satisfy have nonlinear property of the ball and beam system. The proposed algorithm is based on a fuzzy logic control system using a neural network learinign algorithm which is a back-propagation algorithm. This system learn membership functions with input variables. The purpose of the design is to control the position of the ball along the track by manipulating the angualr position of the serve. As a result, it is concluded that the neural network control system with fuzzy-neuron is more effective than the conventional fuzzy system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1103-1107
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• 2016

Network assets have been protected from malware infection by checking the integrity of mobile devices through network access control systems, vaccines, or mobile device management. However, most of existing systems apply a uniform security policy to all users, and allow even infected mobile devices to log into the network inside for completion of the integrity checking, which makes it possible that the infected devices behave maliciously inside the network. Therefore, this paper proposes a network defense mechanism based on isolated networks. In the proposed mechanism, every mobile device go through the integrity check system implemented in an isolated network, and can get the network access only if it has been validated successfully.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1711-1716
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• 2004

This paper presents identification and control of a 10-m antenna via accelerometers and angle encoder data. Artificial Neural Networks can be used effectively for the identification and control of nonlinear dynamical system such as a large flexible antenna. Some identification results are shown and compared with the results of conventional prediction error method. And we use a neural network inverse model for control the large flexible antenna. In the neural network inverse model, a neural network is trained, using supervised learning, to develop an inverse model of the antenna. The network input is the process output, and the network output is the corresponding process input. The control results show the validation of the ANN approach for identification and control of the 10-m flexible antenna.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.1
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• pp.80-88
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• 2002

As many sensors and actuators are used in automated systems, various industrial networks are adopted for digital control system. In order to take advantages of the networking, however, the network implementation should be carefully designed to satisfy real-time requirements considering network delays. This paper presents the implementation scheme of a networked control system via Profibus-DP network. More specifically, the effect of the network delay on the control performance was evaluated on a Profibus-DP testbed, and a GA-based PID tuning algorithm is proposed to design controllers suitable for networked control systems.