• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.96.2-96
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• 2001

This paper presents a control method for the experimental mobile vehicle. By merging the advantages of neural network, adaptive and fuzzy control, neural network-based adaptive fuzzy control is proposed. It can deal with a large amount of training data by neural network, from these data producing more accurate fuzzy rules by adaptive control, and then controlling the object by fuzzy control. This is not the simple combination of the three methods, but merging them into one control system Experiments and some future considerations are given.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.989-990
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• 2012

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.1-6
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• 2008

The ubiquitous sensor network technique is widely applied to variety of information fields such as home automations, logistics, traffic controls, public administrations, health and environment monitoring and etc. It is particularly useful in the areas where energy consumption is minimal and where continuous monitoring of the surrounding environments, which generates streams of data, are required. In this study, we have designed and implemented a living environment automatic control system which collects the streams of temperature, humidity, light and noise data of a simulated house setting in real-time fashion, then controls the home environment based on the collected data according to the users favorites. In order to differentiate the proposed system from the currently existing similar system, we have demonstrated not only the feasibility of collecting data using sensor network in the controlled environment but also the ability to control the various household equipments through wireless communications.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.169-174
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• 2000

In this paper, Haar wavelet-based neural network is described for the identification and control of discrete-time nonlinear dynamical systems. Wavelets are suited to depict functions with local nonlinearities and fast variations because of their intrinsic properties of finite support and self-similarity. Due to the orthonormal properties of Haar wavelet functions, wavelet neural networks result in a greatly simplified training problem. This wavelet-based scheme performs adaptively both the identification of nonlinear functions and the control of the overall system, while the multilayer neural network is applied to the control system just after its sufficient learning of the unknown functions. Simulation shows that the wavelet network can be a good alternative to a multilayer neural network with backpropagation.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.655-660
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• 2010

As increasing the number of Electronic Control Units in a vehicle, the proportion for reliability and stability of the software is going increasingly. Accordingly, the traditional CAN network has occurred the situation that the requirement of developing vehicle software is not sufficient. To solve these problems, the FlexRay network which is ensured the high bandwidth and real-time is generated. However it is difficult to implement FlexRay based application software because of complex protocol than traditional CAN network. Accordingly the system for analysis and verification of network state is needed. Also vehicle vendor develops application software using Matlab/Simulink in order to increase productivity. But this development method is hard to solve the network problem of node to node. Therefore this paper implements Matlab/Simulink based FlexRay network system and verifies it through comparing with existing embedded system.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.11a
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• pp.81-87
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• 2005

This paper addresses the CAN based networked intelligent multi-motor control system using DSP2812 microprocessor. CAN built in DSP2812 microprocessor is used to control and monitor the multi-motor system with the inverter driving system. CAN network implementation schemes and the algorithm for multi-motor control and monitoring is also developed. We configure the multi-motor control experimental system to verify the proposed algorithm and the reliability of CAN networks system in the various operation of two induction motors. The experimental results show that CAN based networked intelligent multi-motor control system using DSP2812 microprocessor can carry out the real-time network based control in various speed range and the position control of induction motors.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.10
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• pp.58-66
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• 2010

This paper shows an implementation and management result of wireless networks based security light control system, which performs a great role in protection of pedestrians and prevention of crime. Conventional security light units have severe limits in confirmation and inspection of security light unit failure, like wilful damage by someone or failure by influence of other equipment or failure by spontaneous heat-increase, and so on. In addition, local government offices are responsible for maintenance of security light units and as a matter of fact, most of civil complaints are about security light units. It is an obvious that the existing security light maintenance system reaches the limit and the security light maintenance problem is a difficulty of local government. Therefore, efficient security light control system is needed, which enables central control and intelligent maintenance. Moreover, the system has to be easy to control and has to be stable. In this study, wireless sensor network based security light control system is implemented, which is independent of programming language and platform, and which is simple to control and extend the system. The proven protocols, HTTP and SOAP, are utilized in order to improve the system reliability. This paper shows the excellence of our proposed system by implementing and operating it in real environment.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.135-139
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• 2000

This research proposes a nonlinear sliding mode control. The sliding mode control is designed according to Lyapunov function. The equivalent control term is estimated by neural network. To estimate the unknown part in the control law in on-line fashion, A recurrent neural network is given as on-line estimator. The stability of the control system is guaranteed owing to the on-line learning ability of the recurrent neural network. It is certificated through simulation results to be applied to nonlinear system that the function approximation and the proposed control scheme is very effective.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.131-134
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• 1995

We developed a visual system that is able to track the moving objects within a certain range of errors. The visual system is driven by two DC servo motors that are controlled by a computer based on the visual data obtained from a CCD video camera. The software to track the moving objects is developed based on the PWM of the DC motors. Also, the problems how to implement a fuzzy logic control method and a neural network in this system, are also considered in order to check the control performance of tracking. The fuzzy logic algorithm is a powerful control technique for nonlinear dynamical system and also the neural network could be implemented in this system. In this paper, we present configuration of tracking system developed in our laboratory, the control methods of the visual system and the experimental results are shown.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1165-1168
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• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Network(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Radial Basis Function Networks(RBFN). The learning with RBFN is fast and precise. This paper discusses RBFN as identification procedure is based on a nonlinear dynamical systems. and A design method of model follow control system based on RBFN controller is developed. As a result of applying this method to inverted pendulum, the simulation has shown that RBFN can be used as identification and control of nonlinear dynamical systems effectively.