• Journal of Institute of Control, Robotics and Systems
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• v.6 no.9
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• pp.758-767
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• 2000

The properties of linear time-varying(LTV) systems vary because of the time-varying property of plant parameters. The generalized controller design method for linear time-varying systems does not exit because the analytic soultion of dynamic equation has not been found yet. Hence, to design a controller for LTV systems, the robust control methods for uncertain LTI systems which are the approximation of LTV systems have been generally ised omstead. However, these methods are not sufficient to reflect the fast dynamics of the original time-varying systems such as missiles and supersonic aircraft. In general, both the performance and the robustness of the control system which is designed with these are not satisfactory. In addition, since a better model will give the more robustness to the controlled system, a gain scheduling technique based on LTI controller design methods has been uesd to solve time problem. Therefore, we propose a new gain scheduled QFT method for LTV systems based on neural networks in this paper. The gain scheduled QFT involves gain dcheduling procedured which are the first trial for QFT and are well suited consideration of the properties of the existing QFT method. The proposed method is illustrated by a numerical example.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.5
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• pp.57-64
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• 2004

We proposed the eye gaze tracking system under natural head movements, which consists of one narrow-view field CCD camera, two mirrors which of reflective angles are controlled and active infra-red illumination. The mirrors' angles were computed by geometric and linear algebra calculations to put the pupil images on the optical axis of the camera. Our system allowed the subjects head to move 90cm horizontally and 60cm vertically, and the spatial resolutions were about 6$^{\circ}$ and 7$^{\circ}$, respectively. The frame rate for estimating gaze points was 10～15 frames/sec. As gaze mapping function, we used the hierarchical generalized regression neural networks (H-GRNN) based on the two-pass GRNN. The gaze accuracy showed 94% by H-GRNN improved 9% more than 85% of GRNN even though the head or face was a little rotated. Our system does not have a high spatial gaze resolution, but it allows natural head movements, robust and accurate gaze tracking. In addition there is no need to re-calibrate the system when subjects are changed.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.761-762
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• 2006

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.607-612
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• 2000

Microprecision agriculture for a fully controlled plant factory is proposed in this paper. Microprecision agriculture can be attained by using plant factories to realize profitable alternative agriculture. A closed, fully controlled, plant-growing factory is far better in terms of minimizing all sorts of waste. The limit and optimum design concept has to be applied to establish an economically feasible, fully controlled, plant-growing factory. To achieve this objective, microprecision technologies have to be developed. Microprecision technologies should be involved in sensing, modeling, controlling, and collecting information for the mechatronics for plant production. Basic technologies for microprecision are already available; they are SPA (speaking plant approach to environmental control), AI (artificial intelligence: expert systems, neural networks, genetic algorithms, photosynthetic algorithms etc.), bioinstrumentation, non-invasive measurement, biomechatronics, and biorobotics. A microprecision irrigation system for plug production is an example of a microprecision technology that has actually been implemented in a plug seedling production factory.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.27-35
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• 2021

Artificial life is used in various fields of applied science by evaluating natural life-related systems, their processes, and evolution. Research has been actively conducted to evolve physical body design and behavioral control strategies for the dynamic activities of these artificial life forms. However, since co-evolution of shapes and neural networks is difficult, artificial life with optimized movements has only one movement in one form and most do not consider the environmental conditions around it. In this paper, artificial life that co-evolve bodies and neural networks using predator-prey models have environmental adaptive movements. The predator-prey hierarchy is then extended to the top-level predator, medium predator, prey three stages to determine the stability of the simulation according to initial population density and correlate between body evolution and population dynamics.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.261-270
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• 1999

Sintering transforms powdered ore into lumped ore so that the latter can be used in a blast furnace. The powdered ore combined with coke and other materials is loaded into a container and moved along by a pallet while the ignited coke bums. The speed by which the pallet moves determines how much sintering takes place. Since the process is complicated and lacks an accurate mathematical model, human operators manually control the speed by monitoring various factors in the plant. In this paper, we propose a neural network-based pallet speed controller which copies human operator knowledge. Actual process data were collected from a sintering plant fer eight months and preprocessed to remove noisy and inconsistent data. A multilayer perceptron was trained using a back-propagation learning algorithm. In on-line testing at the sinter plant, the proposed model reliably controlled pallet speed during normal operation without the help of human operators. Moreover, the duality and productivity was as good as with human operators.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.261-270
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• 1999

Sintering transforms powdered ore into lumped ore so that the latter can be used in a blast furnace. The powdered or combined with coke and other materials is loaded into a container and moved along by a pallet while the ignited coke burns. The speed by which the pallet moves determines how much sintering takes place. Since the process is complicated and lacks an accurate mathematical model, human operators manually control the speed by monitoring various factors in the plant. In this paper, we propose a neural network-based pallet speed controller which copies human operator knowledge. Actual process data were collected from a sintering plant for eight months and preprocessed to remove noisy and inconsistent data. A multilayer perceptron was trained using a back-propagation learning algorithm. In on-line testing at the sinter plant, the proposed model reliably controlled pallet speed during normal operation without the help of human operators. Moreover, the quality and productivity was as good as with human operators.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1120-1123
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• 2003

This paper is proposed a ANN-based rotor position and speed estimation method for IPMSM by measuring the currents. Because the proposed estimator treats the estimated motor speed as the weights, it is possible to estimate motor speed to adapt back propagation algorithm with 2 layered neural network. The proposed control algorithm is applied to IPMSM drive system. The operating characteristics controlled by neural networks control are examined in detail.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.45-53
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• 2023

This study constructs various scenarios required for landscape lighting; furthermore, a large-capacity general-purpose multifunctional controller is designed and implemented to validate the operation of the various scenarios. The multi-functional controller is a large-capacity general-purpose controller composed of a drive and control unit that controls the scenarios and colors of LED modules and an LED display unit. In addition, we conduct a computer simulation by designing a control system to represent the most appropriate color according to the input values of the temperature, illuminance, and humidity, using the neuro-control system. Consequently, when examining the result and output color according to neuro-control, unlike existing crisp logic, neuro-control does not require the storage of many data inputs because of the characteristics of artificial intelligence; the desired value can be controlled by learning with learning data.