• Proceedings of the KIEE Conference
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• 2004.10a
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• pp.182-184
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• 2004

This paper presents a PI controller tuning method for high performance permanent magnet synchronous motor (PMSM) drives under load variations using clustering fuzzy algorithm. In many speed tracking control systems PI controller has been used due to its simple structure and easy of design. PI controller, however, suffers from the electrical machine parameter variations and disturbances. In order to improve the tracking control performance under load variations, the PI controller parameters are modified during operation by clustering fuzzy method. This method based on optimal fuzzy logic system has simple structure and computational simplicity. It needs only sample data which is obtained by optimal controller off-line. As the sample data implemented in the adaptive fuzzy system can be modified or extended, a flexible control system can be obtained Simulation results show the usefulness of the proposed controller.

• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.97-99
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• 2007

This paper presents a fuzzy logical control method to implement an on-line optimum efficiency control for Permanent Magnet Synchronous Motor. This method real-timely adjusts the output voltage of the inverter system to achieve the optimum running efficiency of the whole system. At first, the input power is calculated during the steady state in the process of efficiency optimizing. To exactly estimate the steady state of the system, this section needs check up the speed setting on timely. The second section is to calculate input power of dc-bus. The exact measurement of the voltage and current is the vital point to acquire the input power. The third section is the fuzzy logic control unit, which is the key of the whole drive system. Based on the change of input power of dc-bus and output voltage, the variable of output voltage is gained by the fuzzy logical unit. With the on-line optimizing. the whole system call fulfill the minimum input power of dc-bus on the running state. The experimental result proves that the system applied the adjustable V/f control method and the efficiency-optimizing unit possesses optimum efficiency, and it is a better choice for simple variable speed applications such as fans and pump.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.125.5-125
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• 2001

Until now, many guidance laws have been developed. They mainly used the classical tail-pursuit guidance method based on geometric angle information, the proportional navigation method based on the line of sight(LOS) rate, and the optimal guidance law based on optimal control theorem. In the augmented guidance law, target acceleration information and autopilot characteristics are added the guidance command. In this study, new guidance laws considering geometric angle are proposed. Two guidance laws are developed for the midcourse guidance law, and a guidance law is developed for the terminal guidance respectively. The proposed guidance laws utilize the LOS rate and the geometric angle information simultaneously. In the midcourse guidance, the guidance command is ...

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2187-2189
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• 2003

This paper presents a design method of the wavelet neural network(WNN) controller based on a direct adaptive control scheme for the intelligent control of chaotic systems. The conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on a direct adaptive control method is proposed to control chaotic systems whose mathematical models are not available. The gradient-descent method is used for training a wavelet neural network controller. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with applications to the chaotic system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.123.1-123
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• 2001

For Autonomous navigation of the mobile robots, the robots' capability to recognize 3D environment is necessary. In this paper, an on-line 3D map building method for autonomous mobile robots is proposed. To get range data on the environment, we use an sensor system which is composed of a structured light and a CCD camera based on optimal triangulation. The structured laser is projected as a horizontal strip on the scene. The sensor system can rotate $\pm$ $30{\Circ}$ with a goniometer. Scanning the system, we get the laser strip image for the environments and update planes composing the environment by some image processing steps. From the laser strip on the captured image, we find a center point of each column, and make line segments through blobbing these center poings. Then, the planes of the environments are updated. These steps are done on-line in scanning phase. With the proposed method, we can efficiently get a 3D map about the structured environment.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.123.5-123
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• 2001

For autonomous navigation of the mobile robots, the robots' capability to recognize 3D environment is necessary. In this paper, an on-line 3D map building method for autonomous mobile robots is proposed. To get range data on the environment, we use a sensor system which is composed of a structured light and a CCD camera based on optimal triangulation. The structured laser is projected as a horizontal strip on the scene. The sensor system can rotate$\pm$30$^{\circ}$ with a goniometer. Scanning the system, we get the laser strip image for the environments and update planes composing the environment by some image processing steps. From the laser strip on the captured image, we find a center point of each column, and make line segments through blobbing these center points. Then, the planes of the environments are updated. These steps are done on-line in scanning phase. With the proposed method, we can efficiently get a 3D map about the structured environment.

• 한국생물공학회:학술대회논문집
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• 2000.04a
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• pp.173-176
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• 2000

A neural network based controller (NN controller) was studied for the control of ammonia concentrations in biological processes. An ammonia FIA has been employed to on-line monitor the concentrations of ammonia in a bioreactor. The optimal neural network structure was investigated by computer simulation and found to be a 3(inputlayer)-2(hidden layer)-1(output layer). The NN controller had advantage over the PID controller, even though the former is more time consuming. The 3-2-1 NN controller has been used to control the ammonia concentrations in a simulated bioprocess and also in a real cultivation process of yeast, and its performance were investigated.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.3
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• pp.343-348
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• 2010

Recently, unmanned electric vehicles are increasingly interested among all of the world since they can provide low exhaust gas, high efficiency and high mobility. To exploit their silent maneuver and high mobility, unmanned electric vehicles have been developed since early 1980's for military. However, it is not easy to design and control a power system satisfying operating duration and mobility performance requirements based on various mission profiles for military use under the conditions of limited space and weight. Moreover it is also necessary to prevent over-charge, over-discharge and voltage unbalance between cells of battery to secure high voltage battery which is serially connected with muti-cells. In this paper, we presents power control and optimal management method for the dog-horse robot which adopts a electric power system and suggests a guide-line to manage and control to secure high voltage battery.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.378-380
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• 1996

This paper presents the novel control and analysis of GTO-CSC. The control method is based on the linearization of an optimal modulation strategy so that the turn-on-periods of the GTO switches can be computed in real-time for any specified modulation index. These PWM patterns allow to produce minimal ac line current low order harmonics of ac line current and low switchings. Finally, the computer simulation results are presented to verify the theoretical analysis.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.56-66
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• 1998

This paper investigates a neuro-controller combined in parallel with a conventional linear controller of PID type in order to control nonminimum phase systems more efficiently. The objective is to minimize overall position errors as well as to maintain small undershooting. A costfunction is proposed with two conflict objectives. The neuro-controller is trained off-line with evolutionary programming(EP) in such a way that it becomes optimal by minimizing the given cost function through global evaluation based on desired control performance during the whole training time interval. However, it is not easy to find an optimal solution which satisfies individual objective simultaneously. With the concept of Pareto optimality and EP, we train the proposed controller more effectively and obtain a valuable set of optimal solutions. Simulation results show the efficacy of the proposed controller in a viewpoint of improvement of performance of a step response like fast settling time and small undershoot or overshoot compared with that of a conventional linear controller.