• Journal of the Korean Society of Industry Convergence
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• v.17 no.2
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• pp.69-76
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• 2014

This paper proposes 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.48
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• pp.145-152
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• 1998

Driving performance is characterized by many things such as driver's experience period, age, ability of information processing and reaction time of control devices and so forth. However, each factor of driving performance is needed to help and screen a poor driver for safe driving. In this paper, driving performance was estimated by reaction of manipulating brake, accelerator, steering wheel and speed. Subjects were grouped by experience of accident and age. Combinations of every group were analysed. For all the dependent variables, only steering wheel and speed were shown to have significant difference, which could be regarded as visual information of speed and direction were the important factors to drive safely. Especially for tile elderly, it is needed to enhance their ability of visual information processing that is to be decreased with aging. Therefore driving simulator to train and screen the poor driver should be studied.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.312-318
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• 2003

We propose a new technique for the cruise control system design of a mobile robot with three 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 teaming architecture. It is proposed a learning controller consisting of too 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 three independent wheels.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.2
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• pp.18-28
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• 2001

Super computers has been utilized to carry out vehicle dynamics in real time. This research propose an implicit integra-tion method for vehicle state variables. Newton chord method is empolyed to solve the equations of motion and con-straints. The equations of motion and constraints are formulated such that the Jacobian matrix for Newton chord method is needed to be computed only once for a dynamic analysis. Numerical experiments showed that the Jacobian matrix generat-ed at the initial time could have been utilized for the Newton chord iterations throughout simulations under various driving conditions. Convergence analysis of Newton chord method with the proposed Jacobian updating method is carried out. The proposed algorithm yielded accurate solutions for a prototype vehicle multibody model in realtime on a 400 MHz PC compatible.

• Aerospace Engineering and Technology
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• v.6 no.2
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• pp.236-244
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• 2007

The 40% scaled vehicle of Smart UAV has been developed for the investigation of basic flight characteristics and the verification of flight control algorithm. The similar gimbal hub and drive train with the full scale UAV were implemented and a forced air cooling reciprocating engine was installed. The various kind of tests were conducted for the major components of the vehicle. The important performance and mechanical endurance of the fabricated vehicle were identified by ground and hovering test.

• 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 KIPE Conference
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• 2006.06a
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• pp.4-6
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• 2006

본 연구에서는 열차 추진 장치 시스템에 엔진의 중심부를 모의할 수 있는 Simplorer의 프로그램을 개발하여 M-G Set으로 모사한다. 열차의 엔진부와 발전기 구동에서의 제어는 DTC(Direct Torque Control) 제어로 모사 하였다. M-G Set을 구성은 견인력 토크지령을 받아 엔진부에서 DTC제어를 하였고, 발전기 구동 부는 속도지령을 받는 DTC제어로 하여 추진 장치 성능시험설비 열차운행모의에 의한 전력 조류 해석, 소비 및 회생전력을 분석할 수 있도록 하였다.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.36.2-36.2
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• 2011

In variable-speed variable-pitch wind turbines, the conventional approach for controlling power-production operation relies on a generator-torque controller and a rotor-collective blade-pitch controller. Both controllers use the generator speed measurement as the sole feedback input. In order to mitigate unwanted excitation of the control system, many filters are adopted. In this study, the characteristics of some filters for signal processing are investigated based on frequency response function. They include low-pass filters, band-pass filters, and notch filters. Especially, this study focuses on design parameters of their filters.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.1
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• pp.6-12
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• 2007

In this paper, an robust adaptive backstepping controller is proposed for the speed control of separately excited DC motor in pure electric vehicles. A general electric drive train of PEV is conceptually rearrange to major subsystems as electric propulsion, energy source, and auxiliary subsystem and the load torque is modeled by considering the aerodynamic, rolling resistance and grading resistance. Armature and field resistance, damping coefficient and load torque are considered as uncertainties and noise generated at applying load torque to motor is also considered. It shows that the backstepping algorithm can be used to solve the problems of nonlinear system very well and robust controller can be designed without the variation of adaptive law. Simulation results are provided to demonstrate the effectiveness of the proposed controller.