• Proceedings of the Korean Society of Marine Engineers Conference
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• 2000.05a
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• pp.11-15
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• 2000

This paper provides a basic study on automatic of a trailer type vehicle which consists of two parts such as a tractor and a trailer Backward moving and parking control is very important to automate this type of vehicle. However it is very difficult to control such their motion since a trailer type vehicle is a non-holonomic system. Therefore in this paper we propose the backward path tracking control algorithm for a trailer type vehicle. And also this paper presents the results of simulation to verify the effectiveness of the proposed control algorithm.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.1
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• pp.124-130
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• 2001

This paper presents a methodology on automation of a trailer type vehicle which consists of two parts: a tractor and a trailer. Backward moving and parking control is very important to automate this type of vehicle. It is difficult to control the motion such a trailer vehicle whose dynamics in non-holonomic. Therefore, in this paper, the modeling and parameter estimation of the system using a RCGA(real-coded genetic algorithm) is proposed and a backward path tracking control algorithm is then obtained. The simulation results verify the effectiveness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.717-722
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• 2001

This paper presents a methodology on the backward path tracking control of a trailer type robot which consists of two parts: a tractor and a trailer. It is difficult to control the motion of a trailer vehicle since its dynamics is non-holonomic. Therefore, in this paper, the modeling and parameter estimation of the system using a real-coded genetic algorithm(RCGA) is proposed and a backward path tracking control algorithm is then obtained based on the linearized model. Experimental results verify the effectiveness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.723-732
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• 2001

In this paper, the neurofuzzy controller of optically guided AGV is proposed to improve the path-tracking performance A differential steered AGV has front-side and rear-side optical sensors, which can identify the guiding path. Due to the discontinuity of measured data in optical sensors, optically guided AGVs break away easily from the guiding path and path-tracking performance is being degraded. Whenever the On/Off signals in the optical sensors are generated discontinuously, the motion errors can be measured and updated. After sensing, the variation of motion errors can be estimated continuously by the dead reckoning method according to left/right wheel angular velocity. We define the estimated contour error as the sum of the measured contour in the sensing error and the estimated variation of contour error after sensing. The neurofuzzy system consists of incorporating fuzzy controller and neural network. The center and width of fuzzy membership functions are adaptively adjusted by back-propagation learning to minimize th estimated contour error. The proposed control system can be compared with the traditional fuzzy control and decision system in their network structure and learning ability. The proposed control strategy is experience through simulated model to check the performance.