• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1264-1272
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• 1993

The software for unmanned control of three row typed rice combine has been developed using fuzzy logic. Three fuzzy variables were used : operating status of combine, steering, and speed. Eleven fuzzy rules were constructed and the eleven linguistic variables were used for the fuzzy rules. Six sensors were use of to get input values and sensor input values were quantified into 11 levels. The fuzzy output was infered with fuzzy inferrence which uses the correlation product encoding , and it must have been defuzzified by the method of center of gravity to use it for the control. The result of performance test using graphic simulation showed that the intelligently unmanned control of a rice combine was possible using fuzzy logic control.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.707-714
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• 2000

For self-guiding track-type orchard sprayer, a low-cost steering controller was developed using two ultrasonic sensors, two DC motors and 80196kc microprocessor. The operating principle of controller was to travel the sprayer between artificial targets such as wood stick placed every 1 m along both sides of the demanded path of speed sprayer. Measuring distances to both targets ahead with the ultrasonic sensors mounted on the front end of sprayer, the controller could determine how much offset the position of sprayer was laterally. Then the steering angle was calculated to actuate DC motors connected to the steering clutches, where the fuzzy control algorithm was used. Equipped with the controller developed in this research, the sprayer could be traveled along demanded path, the centerline between targets, at speeds of up to 0.4m/sec with an accuracy of ${\pm}$20cm.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.517-523
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• 2002

In this paper, we propose system that apply VIBL method to add speech recognition to LIBL method based on human s studying method to use natural language to steering system of ship, MERCS and winch appliances and use VIBL method to alternate process that linguistic instruction such as officer s steering instruction is achieved via ableman and control steering gear, MERCS and winch appliances. By specific method of study, ableman s suitable steering manufacturing model embodies intelligent steering gear controlling system that embody and language direction base studying method to present proper meaning element and evaluation rule to steering system of ship apply and respond more efficiently on voice instruction of commander using fuzzy inference rule. Also we embody system that recognize voice direction of commander and control MERCS and winch appliances. We embodied steering manufacturing model based on ableman s experience and presented rudder angle for intelligent steering system, compass bearing arrival time, evaluation rule to propose meaning element of stationary state and correct steerman manufacturing model rule using technique to recognize voice instruction of commander and change to text and fuzzy inference. Also we apply VIBL method to speech recognition ship control simulator and confirmed the effectiveness.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.5
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• pp.82-91
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• 2003

Vehicle dynamics control (VDC) has been a breakthrough and become a new terminology for the safety of a driver and improvement of vehicle handling. This paper examines the usefulness of a brake steer system (BSS), which uses differential brake forces for steering intervention in the context of VDC, In order to help the car to turn, a yaw moment can be achieved by altering the left/right and front/rear brake distribution. The steering function achieved through BSS can then be used to control lateral position in an unintended road departure system. An 8-DOF non-linear vehicle model including STI tire model will be validated using the equations of motion of the vehicle, and the non-linear vehicle dynamics. Since fuzzy logic can consider the nonlinear effect of vehicle modeling, fuzzy controller is designed to explore BSS feasibility, by modifying the brake distribution through the control of the yaw rate of the vehicle. The control strategies developed will be tested by simulation of a variety of situation; the possibility of VDC using BSS is verified in this paper.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.1
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• pp.109-115
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• 2010

This paper deals with trajectory control of computer simulated mobile robot via fuzzy control. Mobile robot is controlled by Mamdani type fuzzy controller. Inputs of the fuzzy controller are angle between mobil robot and target, changed angle and output is the steering angle, which is control input. Fuzzy rules have seven rules and are selected by human experiential knowledge. Also we propose a scaling factors tuning scheme which is the another focus in designing fuzzy controller. In this paper, we adapt the RCGA which is well known in parameter optimization to adjust scaling factors. The simulation results show that the fuzzy control effectively realize trajectory stabilization of the mobile robot along a given reference target from various initial steering angles.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.142-147
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• 1999

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, 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 simulation for trajectory tracking of the speed and azimuth of a track vehicle.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.7
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• pp.21-31
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• 1996

An intelligent integrated control system is designed for the active steering and the left/right traction force distribution control of electric vehicles, where input-output linearization is employed. Also, a fuzzy-rule-based cornering force estimator is suggested to avoid using an uncertain highly nonlinear expression, and a neural network compensator is additively utilized for the estimator to correctly find cornering forece. With these techniques, the proposed control system is shown by simulation results to be robust against drastic change of the external environments such as road conditions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.133-139
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• 1997

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is propored a learning controller consisting of two neural network-fuzzy based on independent resoning and a connection net with fixed weights to simply the neural network-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.70-75
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• 1997

This paper proposes a new approach to the design of fuzzy-neuro control for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, 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 of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• Journal of Biosystems Engineering
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• v.25 no.1
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• pp.55-62
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• 2000

Autonomous farm operation needs to be developed for safety, labor shortage problem, health etc. In this research, an autonomous tractor for tillage was investigated using machine vision and a fuzzy logic controller(FLC). Tractor heading and offset were determined by image processing and a geomagnetic sensor. The FLC took the tractor heading and offset as inputs and generated the steering angle for tractor guidance as output. A color CCD camera was used fro the image processing . The heading and offset were obtained using Hough transform of the G-value color images. 15 fuzzy rules were used for inferencing the tractor steering angle. The tractor was tested in the file and it was proved that the tillage operation could be done autonomously within 20 cm deviation with the machine vision and the FLC.