• Proceedings of the KWS Conference
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• 2004.05a
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• pp.21-23
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• 2004

파이프용접에서 특정용접을 하기 위한 최적의 용접조건 선정하는 작업은 대개 많은 시간과 비용을 요구한다. 최근에 인공지능(AI) 기술을 이용하여 용접변수를 결정하기 위해서는 생산성, 용접결함 등 여러 가지 요소를 고려해야 한다고 주장한다. (중략)

• Proceedings of the SAREK Conference
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• 2003.07a
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• pp.171-171
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• 2003

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.4
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• pp.98-106
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• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the cower and iron losses. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. There exists a variety of combinations of d and f-axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the hybrid artificial intelligent(HAI) controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm

• Journal of the Korean Society of Safety
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• v.15 no.3
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• pp.115-123
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• 2000

Pneumatic actuators are widely used in a variety of hazardous working environments. Any process that involves pneumatic actuation is also recognized as "eco-friendly". In most cases, applications of pneumatic actuators require only point-to-point control. In recent years, research efforts have been directed toward achieving precise position tracking control. In this study, a tracking position control method is proposed and experimentally evaluated for a linear positioning system. The positioning system is composed of a pneumatic actuator and a 3-port proportional valve. The proposed controller has an inner pressure control loop and an outer position control loop. A PID controller with feedback linearization is used in the pressure control loop to nullify the nonlinearity arising from the compressibility of the air. The position controller is also a PID controller augmented with the friction compensation by a neural network. Experimental results indicate that the proposed controller significantly improves the tracking performance.rformance.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.779-782
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• 1997

In this paper, accurate position control of a stenciling robot manipulator is designed. The stenciling robot is requried to draw lines and characters on the pavement. Since the robot is huge and heavy, the inertia is expected to play a major role in the tracking performance as desired. Here we are proposing neural network control scheme for a computed-torque like controller for the stenciling robot. On-line compensation is achieved by neural network. Simulation studies with stenciling robot are carried out to test the performance of the proposed control scheme.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.625-628
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• 1997

In this paper the new neural network and sliding mode suspension controller is proposed. That neural network is error self-recurrent neural network. For fast on-line learning, this paper use recursive least squares method. A new neural networks converges considerably faster than the backpropagation algorithm and has advantages of being less affected by the poor initial weights and learning rate. The controller for suspension systems is designed according to sliding mode technique based on new proposed neural network.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.147-150
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• 1996

This paper describes controller design and implementation method for controlling the temperature distribution in a thermal stratified wind tunnel(TSWT) by using a neural network algorithm. It is impossible to derive a mathematical model of the relation between heat inputs and temperature outputs in the test section of the TSWT governed by a nonlinear turbulent flow. Thus inverse neural network models with a multi layer perceptron structure are used in a feedforward control loop and feedback control loop to generate an arbitrary temperature distribution in the test section of the TSWT.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1261-1264
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• 1996

This paper presents the design of a neural network based PWM technique for a three level inverter of electric trains. A three-level inverter has several advantages compared with a two-level inverter in this application. In viewpoint of correcting unbalance of DC-link voltage, a novel method is developed and verified in computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1169-1172
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• 1996

A time-optimal control law for quick, strongly nonlinear systems has been developed and demonstrated. This procedure involves the utilization of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the known and unknown systems with constrained inputs and/or states. The nature of neural networks as a parallel processor would circumvent the problem of "curse of dimensionality". The control law has been demonstrated for a velocity input type motor identified by a genetic algorithm called GENOCOP.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1006-1011
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• 1991

In this paper, a neural network estimator which estimates the output of the wide range oxygen sensor is proposed, The neural network estimator is constructed to give the output of the wide range oxygen sensor from rpm, fuel injection time, throttle position, and output voltage of the exhaust gas oxygen sensor. And, using this estimator, PI controller for air-fuel ratio control is designed. Experiment results show that the proposed method gives good results for SONATA engine under light load and constant rpms.