• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1730-1731
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• 2007

In this paper, we present a direct adaptive control method using neural networks for the control of nonlinear systems. The weights of neural networks are trained by an adaptive backpropagation algorithm based on Lyapunov stability theory. We develop the parameter update-laws using the neural network input and the error between the desired output and the output of nonlinear plant to update the weights of a neural network in the sense that Lyapunove stability theory. Beside the output tracking error is asymptotically converged to zero.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1189-1198
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• 2017

Computer-aided design (CAD)-based Monte Carlo radiation transport is of value to the nuclear engineering community for its ability to conduct transport on high-fidelity models of nuclear systems, but it is more computationally expensive than native geometry representations. This work describes the adaptation of a rendering data structure, the signed distance field, as a geometric query tool for accelerating CAD-based transport in the direct-accelerated geometry Monte Carlo toolkit. Demonstrations of its effectiveness are shown for several problems. The beginnings of a predictive model for the data structure's utilization based on various problem parameters is also introduced.

• Journal of Electrical Engineering and information Science
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• v.1 no.1
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• pp.39-44
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• 1996

In this paper, adaptive controller with switching action is designed for rigid body robot manipulators to ensure the uniform stability of the manipulator system without a priori knowledge of the unmodeled dynamics. It will be shown that the parameter estimates are bounded independent of the other closed-loop signals boundedness, and also shown that the tracking error belongs to the normalized error bound via mathematical analisys. The robustness and performance of the proposed adaptive controller is investigated for the two-link direct drive manipulator actuated by VRM(Variable Reluctance Motor).

• The Journal of the Acoustical Society of Korea
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• v.39 no.3
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• pp.163-169
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• 2020

The asynchronous bistatic sonar needs to estimate direct blast arrival time at a receiver to localize targets, and therefore the direct blast arrival time estimation error could be added to target localization error in comparison with synchronous system. Direct blast especially appears as several peaks at the matched filter output by multipath, thus we compared the first peak detection technique and the maximum peak detection technique of those peaks for direct blast arrival time estimation through sea trial data. The test was performed in a shallow sea with bistatic sonar made up of spatially separated source and line array sensors. Line array sensors obtained the target signal which is generated from the echo repeater. As a result, the first peak detection technique is superior to maximum peak detection technique in direct blast arrival time estimation error. The result of this analysis will be used for further research of target tracking in the asynchronous bistatic sonar.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.9-13
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• 2022

In this paper, the tracking error of monopulse radar caused by cross-eye jamming using terrain bounce is analyzed. Cross-eye jamming is a method of generating an error in a radar tracking system by simultaneously transmitting two signals with different phases and amplitudes. When the monopulse radar receives the cross-eye jamming signal generated by the terrain bounce, a tracking error occurs in the elevation direction. In the presence of multipath, this signal is a combination of the direct target return and a return seemingly emanating from the target image beneath the terrain surface. Terrain bounce jamming has the advantage of using a single jammer, but the space affecting the jamming is limited by the terrain reflection angle and the degree of scattering of the terrain. This study can be usefully used to protect ships from low-altitude missiles or aircraft in the sea.

• Progress in Medical Physics
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• v.20 no.3
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• pp.174-179
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• 2009

To track moving tumor in real time, CyberKnife system imports a technique of the synchrony respiratory tracking system. The fiducial marker which are detectable in X-ray images were demand in CyberKnife Robotic radiosurgery system. It issued as reference markers to locate and track tumor location during patient alignment and treatment delivery. Fiducial marker implantation is an invasive surgical operation that carries a relatively high risk of pneumothorax. Most recently, it was developed a direct lung tumor registration method that does not require the use of fiducials. The purpose of this study is to measure the accuracy of target applying X-sight lung tracking using the Gafchromic film in dynamic moving thorax phantom. The X-sight Lung Tracking quality assurance motion phantom simulates simple respiratory motion of a lung tumor and provides Gafchromic dosimetry film-based test capability at locations inside the phantom corresponding to a typical lung tumor. The total average error for the X-sight Lung Tracking System with a moving target was $0.85{\pm}0.22$ mm. The results were considered reliable and applicable for lung tumor treatment in CyberKnife radiosurgery system. Clinically, breathing patterns of patients may vary during radiation therapy. Therefore, additional studies with a set real patient data are necessary to evaluate the target accuracy for the X-sight Lung Tracking system.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.316-321
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• 2003

This paper presents a design method of the wavelet neural network based controller using direct adaptive control method to deal with a stable intelligent control of chaotic systems. The various uncertainties, such as mechanical parametric variation, external disturbance, and unstructured uncertainty influence the control performance. However, 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 direct adaptive control method with adaptive learning rates is proposed to control chaotic nonlinear systems whose mathematical models are not available. The adaptive learning rates are derived in the sense of discrete-type Lyapunov stability theorem, so that the convergence of the tracking error can be guaranteed in the closed-loop system. In the whole design process, the strict constrained conditions and prior knowledge of the controlled plant are not necessary due to the powerful learning ability of the proposed intelligent control system. The gradient-descent method is used for training a wavelet neural network controller of chaotic systems. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with application to the chaotic systems.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.3
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• pp.238-248
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• 2016

In this study, an automatic command mode transition strategy of direct power control (DPC) is proposed for permanent magnet synchronous generators (PMSGs) medium-voltage (MV) offshore wind turbines (WTs). Benchmarking against the control methods are performed based on a three-level neutral-point-clamped (NPC) back-to-back type voltage source converter (VSC). The ramping rate criterion of complex power is utilized to select the switching vector in DPC for a three-level NPC converter. With a grid command and an MPPT mode transition strategy, the proposed control method automatically controls the generated output power to satisfy a grid requirement from the hierarchical wind farm controller. The automatic command mode transition strategy of DPC is confirmed through PLECS simulations based on Matlab. The simulation result of the automatic mode transition strategy shows that the proposed control method of VOC and DPC achieves a much shorter transient time of generated output power than the conventional control methods of MPPT and VOC under a step response. The proposed control method helps provide a good dynamic performance for PMSGs MV offshore WTs, thereby generating high quality output power.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6484-6487
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• 2013

This paper reports the direct controller for nonlinear plants using a neural network. The controller was composed of an approximate controller and a neural network auxiliary controller. The approximate controller provides rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not place too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network was trained and the system showed stable performance for the inputs it has been trained for. The simulation results showed that it was quite effective and could realize satisfactory control of the nonlinear system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.370-374
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• 1999

The proposed algorithm used the Hybrid teaming rule in the input and hidden layer, and Back-Propagation teaming rule in the hidden and output layer. From the results of simulation of tracking control with one link manipulator as a plant, we verify the usefulness of the proposed control method to compare with common direct adaptive neural network control method; proposed hybrid teaming rule showed faster loaming time faster settling time than the direct adaptive neural network using Back-propagation algorithm. Usefulness of the proposed control method is that it is faster the learning time and settling time than common direct adaptive neural network control method.