• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.147-149
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• 2007

"Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.372-377
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• 1996

A time-optimal control law for quick, strongly nonlinear systems like revolute robots 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".ity".uot;.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.518-521
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• 1999

This paper describes high speed squirrel cage induction motor drives without speed sensors using neural network based on Kalman filter Learning. High speed motors are receiving inverasing attentions in various applications, because of advantages of high speed, small size and light weight with same power level. Larning rate by Kalman filtering is time varying, convergence time fast, effect of initial weight between neurons is small.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.171-174
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• 1999

The torque of SRM is developed by phase currents and inductance variation. Phase currents and inductance variation. Phase current is often the controlled variable in electrical motor drives, so it seems natural to use closed loop current controllers. However, the highly nonlinear nature of switched reluctance motors makes optimisation of closed loop current controlled difficult because of saturation effect in magnetic circuit. Therefore, torque generation region is nonlinearly varied according to phase current and rotor position. This paper describes the torque control scheme with neural network that can control varied with load torque. The torque control is simulated by PSIM.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1154-1160
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• 1999

A rule based controller is constructed for multiple robots accomplishing a given task in unknown environments by using genetic programming. The example task is playing a simplified soccer game, and the controller for robots that governs emergent cooperative behavior is successfully found using the proposed procedure A neural network controller constructed using the rule based controller is shown to be applicable in a more complex environment.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.936-938
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• 1999

In this Paper, the design of a feedback linearization controller using multilayer neural network is proposed. The Proposed feedback linearization control scheme is designed by finding Lie derivatives from an identified neural networks. Lie derivatives are expressed as a combination of weights and neuron outputs. The proposed method is applied to an antenna arm problem and the simulation results show performance comparisons between the ordinary feedback linearization and the Proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.1
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• pp.20-31
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• 1994

FMS(Flesible Manufacturing System), FMX(Flexible Manufacturing Cell)와 같은 유연 생산시스템 뿐만 아니고 공장자동화(FA)의 최하위 단위인 절삭가공 공작기계에 대한 무인화의 실현은 머지않은 장래에 완성될 IMS(Intelligent Manufacturing System)시스템이 구축에 있어서 최대의 걸림돌이 되고 있다. 전통적인 생산시스템에서는 경험을 가진 작업자에 의해 절삭공정이 감시되어지며, 만약 이상이 발생했을 때에는 그 상태에 따른 적절한 조치를 즉시 취할 수 있었다. 그러나 급속도로 연구가 진행되는 무인생산 시스템에서는 이러한 작업자의 역할이 컴퓨터에 의한 자동적인 감시 및 제어 시스템으로 대체되어야 한다. 이러한감시활동 중에서도 공구마모 및 파단의 검출은 효율적인 공구교환정책, 가공물의 품위유지 및 공구와 공작기계의 보호를 위해서 가장 중요한 부분으로 취급되고 있다.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.33-36
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• 2002

In this paper, we propose a control method of an unknown nonlinear system using a dynamical neural network. The method proposed in this paper performs for a nonlinear system with unknown system, identification with using the dynamical neural network, and then a nonlinear adaptive controller is designed with these identified informations. In order to verify the effectiveness of the proposed algorithm, we simulated one-link manipulator. The simulation result showed the effectiveness of using the dynamical neural network in the adaptive control of one-link manipulator.

• Proceedings of the KWS Conference
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• 2002.05a
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• pp.68-70
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• 2002

A CCD camera with a laser strip was applied to realize the automation of welding process in GMAW. The Hough transformation was used to extract the laser stripe and to obtain specific weld points. In this study, a neural network based on the generalized delta rule algorithm was adapted for the process control of GMA, such as welding speed, arc voltage and wire feeding speed.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.247-253
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• 1999

In this paper predictive PID control system using neural network (NNPPID) is proposed to control temperature system. NNPPID is composed of neural network predictor forecasts the future output of plant based on the present input and output of plant. Neural self-tuner yields parameters of PID controller. Experiments prove that NNPPID temperature control system has better performance than conventional PID control.