• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.50-58
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• 2009

For precise tracking control of a servo system with nonlinear friction, a robust friction compensation scheme is presented in this paper. The nonlinear friction is difficult to identify the friction parameters exactly through experiments. Friction parameters can be also varied according to contact conditions such as the variation of temperature and lubrication. Thus, in order to overcome these problems and obtain the desired position tracking performance, a robust adaptive back-stepping control scheme with a dual friction observer is developed. In addition, to estimate lumped friction uncertainty due to modeling errors, a DEKF recurrent neural network and adaptive reconstructed error estimator are also developed. The feasibility of the proposed control scheme is verified through the experiment fur a ball-screw system.

• Journal of Drive and Control
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• v.14 no.4
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• pp.17-22
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• 2017

In this study, the back-drivability of the single-rod and double-rod type EHAs (Electro-Hydrostatic Actuators) was compared by computer simulation and experiments. The back-drivability of EHAs exhibit non-linear behavior like their force tracking performance. In case of the double-rod type EHA, the back-drivability was mostly influenced by the bulk modulus of oil that changes with the working pressure due to entrapped air. The back-drivability of the single-rod type EHA was directly affected by the operation states of its pilot-operated check valves, while the asymmetrical piston geometry and the non-linear bulk modulus of oil also made the dynamic response in building up the cylinder pressure dependent on the operating conditions.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.524-527
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• 1997

Conventional target tracking algorithms based on the linear estimation techniques perform quite efficiently when the target motion does not involve maneuvers. Target maneuvers involving short term accelerations, however, cause a bias in the measurement sequence. Accurate compensation for the bias requires processing more samples of which adds to the computational complexity. The primary motivation for employing a neural network for this task comes from the efficiency with which more features can be as inputs for bias compensation. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates and hence can take the burden off the primary Kalman filter which still provides the target position and velocity estimates.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.1-14
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• 2004

This paper surveys some existing direct adaptive feedback control schemes for linear time-invariant systems with actuator failures characterized by the failure pattern that some inputs are stuck at some unknown fixed or varying values at unknown time instants, and applications of those schemes to aircraft flight control system models. Controller structures, plant-model matching conditions, and adaptive laws to update controller parameters are investigated for the following cases for continuous-time systems: state tracking using state feed-back, output tracking using state feedback, and output tracking using output feedback. In addition, a discrete-time output tracking design using output feedback is presented. Robustness of this design with respect to unmodeled dynamics and disturbances is addressed using a modified robust adaptive law.

• Journal of Power System Engineering
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• v.10 no.3
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• pp.97-103
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• 2006

In this study, a position trajectory tracking control algorithm is proposed for a pneumatic artificial muscle driving apparatus composed of a actuator which imitates the muscle of human, a position sensor and a control valve. The controller applied to the driving apparatus is composed of a state feedback controller and disturbance observer. The feedback controller which feeds back position, velocity and acceleration is derived from the linear model of pneumatic artificial muscle driving apparatus. The disturbance observer is designed to improve trajectory tracking performance and to reduce the effect of model discrepancy. The effectiveness of the designed controller is proved by experiments and the experimental results show that the pneumatic artificial muscle driving apparatus with the proposed control algorithm tracks given position reference inputs accurately.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1299-1306
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• 2017

This paper presents a boundary tracking control of an agent. To this end, it is shown that the boundary tracking problem can be reformulated into a robust control of uncertain double integrator first. Then, a disturbance observer (DOB) based control is proposed solving the robust control problem. Unlike the existing results in the literature, the proposed DOB based control requires only the local position measurement of the boundary (not the gradient information). The performance of the proposed control is demonstrated for two cases: the measurement of the boundary is given in a continuous or discrete manner. Finally, it is shown that the proposed control can be used for environmental monitoring as well by showing that the agent follows a level curve of real environmental monitoring data.

• Journal of KIISE:Information Networking
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• v.35 no.6
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• pp.482-496
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• 2008

Packet classification in the Internet routers requires multi-dimensional search for multiple header fields for every incoming packet in wire-speed, hence packet classification is one of the most important challenges in router design. Hierarchical packet classification is one of the most effective solutions since search space is remarkably reduced every time a field search is completed. However, hierarchical structures have two intrinsic issues; back-tracking and empty internal nodes. In this paper, we propose a new hierarchical packet classification algorithm which solves both problems. The back-tracking is avoided by using the set-pruning and the empty internal nodes are avoided by applying the binary search tree. Simulation result shows that the proposed algorithm provides significant improvement in search speed without increasing the amount of memory requirement. We also propose an optimization technique applying controlled rule copy in set-pruning.

• Journal of KIISE
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• v.42 no.10
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• pp.1239-1246
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• 2015

Cell counting is extensively used to analyze cell growth in biomedical research, and as a result automated cell counting methods have been developed to provide a more convenient and means to analyze cell growth. However, there are still many challenges to improving the accuracy of the cell counting for cells that proliferate abnormally, divide rapidly, and cluster easily, such as cancer cells. In this paper, we present an automated cell counting method for HeLa cells, which are used as reference for cancer research. We recognize and classify the morphological conditions of the cells by using a cell segmentation algorithm based on cell membrane extraction, and we then apply a cell back-tracking algorithm to improve the cell counting accuracy in cell clusters that have indistinct cell boundary lines. The experimental results indicate that our proposed segmentation method can identify each of the cells more accurately when compared to existing methods and, consequently, can improve the cell counting accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.215-218
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• 1997

A dynamic back-propagation neural network is addressed for adaptive neural control system to approximate non-linear control system rather than static networks. It has the capability to represent the approximation of nonlinear system without mathematical analysis and to carry out the on-line learning algorithm for real time application. The simulated results show fast tracking capability and adaptive response by using dynamic back-propagation neurons.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.199-202
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• 2000

RFID (Radio Frequency Identification) is a technique which is used for identifying different types of objects and tracking people and animals. Passive RFID consists of reader, a passive tag. The reader transmit energy to a tag and read information back from tag. The tag is energized by a carrier frequency which is transmitted by the reader and transmit information back to the reader. In this paper, the circuit for read and write RFID system is presented. The presented RFID system adopts 125kHz carrier frequency, backscattering and PSK for communication method.