• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.4
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• pp.62-67
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• 2002

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized loaming architecture. It is Proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tucking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1891-1892
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• 2006

지능형 자동차에 대한 관심이 높아지면서 AICC(Automatic Intelligent Cruise Control)나 무인 자동차가 개발되고 있다. 이러한 기술을 개발하기 위해서는 자동차의 환경과 운항에 관련된 상황을 실시간 감시하는 것이 중요하기 때문에 본 연구에서는 임베디드 시스템을 구성 후 웹 서버를 구현하여 원격지에 존재하는 자동차의 감시 시스템을 개발하고, 자동차 ECU와 인터페이스 하여 엔진 및 자동차의 내부 상황을 웹으로 확인할 있는 임베디드 시스템을 구현하였다. 또한, ECU의 부담과 자동차 Wireless Hamess(W/H)에 의한 중량을 줄이기 위해 블루투스 방식으로 분산제어 방식을 이용 각 부분별 제어할 수 있도록 하였다.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.880-883
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• 1996

This paper suggests a speed control algorithm for the ICC(Intelligent Cruise Controller) system. The speed controller is designed using the fuzzy controller which shows the good performance in nonlinear system having the complex mathematical model. The fuzzy controller was equipped with the capability of a self-learning in real time in order to maintain the good performance of the speed controller in a time-varying environment the self-learning properties and the performance of the fuzzy controller are showed via computer simulation. The suggested fuzzy controller will be applied to the PRV-III which is our test vehicle.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.235-240
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• 2002

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.207-212
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• 2004

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.666-668
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• 1998

An adaptive nonlinear observer-based longitudinal control law for vehicles is presented in this paper. It is assumed that for vehicle i knows only the distance between vehicle i and the preceding vehicle, i-1. An adaptive nonlinear state observer for vehicle i is developed to estimate the velocity and acceleration of the preceding vehicle, i-1. The communication of the position, velocity, and acceleration information is not used in the proposed method. It will be shown by mathematical analysis that the longitudinal control of vehicle can be implemented without an communication of the informations. It will be proven that the observation errors of the nonlinear states converge to zero asymptotically. To show the effectiveness of the proposed method, the simulation results are presented for the longitudinal control of the vehicle.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.2
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• pp.107-113
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• 2005

In this paper, Web server is built up using PXA255, 32bit RISC processor with porting Embedded Linux and GoAhead HTTP(Hyper Text Transfer Protocol) web server, and the system which can monitor and control the environment and condition for AICC(Automatic Intelligent Cruise Control) is realized For sensing the operation condition and change of vehicle the desired data is derived by interfacing ECU(Electric Control Unit) and Embedded system and the rpm of engine is controlled by step motor connected to throttle valve.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.361-366
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• 2012

This paper presents speed control of an electric motorcycle using a fuzzy controller. The electric motorcycle required to meet not only fast throttle response but also stability, when it is on a cruise. However, a 1.5KW (50cc) electric motorcycles selling in the current market are difficult to cruise under the following conditions which are occupant's weight, load weight, wind resistance and road conditions (dirt roads, asphalt road). Because of these reasons, the rapid speed changing occurs in uphill and downhill road. To solve these problems, The input value for Improved fuzzy controller use the speed error and error variance. The output value for improved fuzzy controller uses Q-axis of the motor controlled variable. The D-axis of the motor output for improved fuzzy control uses D-axis controlled variable in proportional to Q-axis controlled variable. Improved fuzzy controller drives the electric motorcycle equipped with IPMSM. The control subject used in this paper is a 1.5KW electric motorcycle equipped with improved fuzzy controller that was used to control the motor speed. To control IPMSM Type of motor torque, D, Q-axis current controller was used. The Fuzzy controller using the proposed algorithm is demonstrated by experimental hardware simulator.

• Journal of the Korean Society of Industry Convergence
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• v.20 no.4
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• pp.313-323
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• 2017

As vehicles become more intelligent this is focused on ways to enhance safety and convenience for both drivers and passengers. In particular, x-by-wire systems that replace rigid mechanical components with dynamically configurable electronic elements are being developed to expand intelligent functions, such as adaptive cruise control or lane departure warning system. Because the malfunction of safety-related modules controlling critical functions, such as brakes, throttle, and steering in x-by-wire systems, can cause injury or death, safety and reliability are the most critical issue for automotive vendors and parts manufacturers. In an effort to develop better and effective brake-by-wire system, this paper presents EMB system by using the low speed electric vehicle, which is not required large braking force, with motor controller. In addition, we design performance evaluation system of EMB with 1/4 low speed electric vehicle model and suggested EMB is evaluated through the performance evaluation system.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1669-1674
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• 2004

In this paper, a nonlinear information filter (IF) for curvilinear motions in an interacting multiple model (IMM) algorithm to track a maneuvering vehicle on a road is investigated. Driving patterns of vehicles on a road are modeled as stochastic hybrid systems. In order to track the maneuvering vehicles, two kinematic models are derived: A constant velocity model for linear motions and a constant-speed turn model for curvilinear motions. For the constant-speed turn model, a nonlinear IF is used in place of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions.