• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.4
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• pp.163-172
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• 2004

Nowadays, to analyze the dynamic characteristics of the automotive driving system, the computer simulation linked up with VR(Virtual Reality) technology is treated as the useful method with the improvement of computing ability. In this paper, the VR simulation system has been developed to investigate the driving characteristics of the ASV(Advanced Safety Vehicle) equipped with an ACC(Adaptive Cruise Control) system. For the purpose, VR environment which generates 3D graphic and sound information of the vehicle, the road, the facilities, and the terrain has been organized for the driving reality. Mathematical models of vehicle dynamic analysis including the ACC model have been constructed for computer simulation. The ACC modulates the throttle and brake functions to regulate the vehicle speed so that vehicles could keep proper spacing. Also, the real-time simulation algorithm synchronizes vehicle dynamic simulation with the graphic rendering. With the developed VR simulation system, simple scenarios are applied to analyze the dynamic characteristics. It is shown that the VR simulation system could be useful to evaluate the adaptive cruise controlled vehicle on various driving conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1759-1766
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• 2010

The performance of noise attenuation of the adaptive active noise control algorithm is improved using the recurrent neural network. The FXLMS that has been frequently used in the active noise control is simple and has low computational load, but this method is weak to nonlinearity of the main or secondary path since it is based on the FIR linear filter method. In this paper, the recurrent neural network filter has been developed and applied to improvement of the active noise attenuation by simulation.

• Proceedings of the KIPE Conference
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• 2004.07b
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• pp.944-948
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• 2004

This paper presents a simple torque estimation method and switching angle control of Switched Reluctance Motor (SRM) using Neural Network (NN). SRM has gaining much interest as industrial applications due to the simple structure and high efficiency. Adaptive switching angle control is essential for the optimal driving of SRM because of the driving characteristic varies with the load and speed. The proper switching angle which can increase the efficiency was investigated in this paper. NN was adapted to regulate the switching angle and nonlinear inductance modelling. Experimental result shows the validity of the switching angle controller.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.33-37
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• 2002

This paper presents a simple torque estimation method and the switching angle control of SRM using Neural Network. SRM has gaining much interest as industrial applications due to the simple structure and high efficiency. Adaptive switching angle control is essential for the optimal driving of a SRM because of the driving characteristic varies with the load and speed. The proper switching angle which can increase the efficiency was investigated in this paper Neural Network was adapted to regulate the switching angle and nonlinear inductance modelling. Experimental result shows the validity of the switching angle controller.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.969-973
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• 1990

This paper presents a method of suboptimal control for nonlinear systems via block pulse transformation. The adaptive optimal control scheme proposed by J.P. Matuszewski is introduced to minimize the performance index. Nonlinear systems are controlled using the obtained optimal control via block pulse transformation. The proposed method is simple and computationally advantageous. Viablity of the this method is established with simulation results for the van der Pol equation for comparision with other methods.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.6
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• pp.509-516
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• 2002

This paper presents a simple torque estimation method and switching angle control of Switched Reluctance Motor(SRM) using Neural Network(NN). SRM has gaining much interest as industrial applications due to the simple structure and high efficiency. Adaptive switching angle control is essential for the optimal driving of SRM because of the driving characteristic varies with the load and speed. The proper switching angle which can increase the efficiency was investigated in this paper. NN was adapted to regulate the switching angle and nonlinear inductance modelling. Experimental result shows the validity of the switching angle controller.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2668-2670
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• 2004

This paper presents an advanced load control method in Direct Load Control(DLC) system. It is important to aggregate a various demand side resource which is surely controllable at the peak power time for a successful DLC system. Because the DLC system use simple On/Off control that may cause a harmful effect on a plant to reduce a peak power load, there are some restriction on deriving a voluntary participation of demand side resource. So it needs a new approach to direct load control method, and this paper describes an advanced load control method using control logic which is based on load properties. This method is easy to take account of a various characteristic of load, it can be use as a dynamic control logic which is good for adaptive control. The suggested control logic method is verified by modeling a control logic for a turbo refrigerator which affects on peak power in summer season.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.213-216
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• 2005

Active control method is applied to a flexible beam excited by a shock impulse by focusing on reducing the residual vibrations after the shock input. It is assumed that the shock input can be measured and is always occurred on the same point of the beam. If the system is well identified and the corresponding inverse system is designed reliably, it has shown that a very simple feed-forward active control method may be applied to suppress the residual vibrations without using an error sensor and adaptive algorithm. Both numerical simulation and experimental result show a promising possibility of applying to a practical problem.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.323-329
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• 2001

In this paper, we present a collaborative method for material delivery using a distributed vehicle agents system. Generally used AGV(Autonomous Guided Vehicle) systems in FA require extraordinary facilities like guidepaths and landmarks and have numerous limitations for application in different environments. Moreover in the case of controlling multi vehicles, the necessity for developing corporation abilities like loading and unloading materials between vehicles including different types is increasing nowadays for automation of material flow. Thus to compensate and improve the functions of AGV, it is important to endow vehicles with the intelligence to recognize environments and goods and to determine the goal point to approach. In this study we propose an interaction method between hetero-type vehicles and adaptive fuzzy logic controllers for sensor-based path planning methods and material identifying methods which recognizes color. For the purpose of carrying materials to the goal, simple color sensor is used instead vision system to search for material and recognize its color in order to determine the goal point to transfer it to. The proposed method reaveals a great deal of improvement on its performance.