• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.548-551
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• 1997

Performance of force tracking impedance control of robot manipulators is degraded by the uncertainties in the robot and environment dynamic model. The purpose of this paper is to improve the controller robustness by applying neural network. Neural networks are designed to learn the uncertainties in robot and environment model for compensating the uncertainties. The proposed scheme is verified through the simulation of 20DOF robot manipulator.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1015-1017
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• 1997

A high impedance fault on the multi-grounded three-phase four-wire distribution system can not be detected by conventional overcurrent sensing devices. In this paper, the neural network is used to detect high impedance faults. The proposed algorithm using back - propagation neural network is demonstrated by simulation with the staged fault test data. The harmonic components of current and the phase of voltage are used as the inputs of neural network. Results of the simulation can be used as a reference for the development of a high impedance fault detector.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.856-858
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• 1999

The analysis of distribution line faults is essential to the proper protection of power system. A high impedance fault(HIF) dose not make enough current to cause conventional protective device. It is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional Protection system. This paper describes an algorithm using neural network for pattern recognition and detection of high impedance faults. Wavelet transform analysis gives the time-scale information. Time-scale representation of high impedance faults can detect easily and localize correctly the fault waveform.

• Proceedings of the KIPE Conference
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• 2017.11a
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• pp.111-112
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• 2017

This paper presents a novel three-port converter for the OnBoard Charger of Electric Vehicles by using an impedance control network. The proposed concept is suitable for charging a main battery and an auxiliary battery of an electric vehicle at the same time due to its power handling capability of the converter without additional switches. The power flow is managed by the phase angle (${\Theta}$) between the ports whereas voltage at each port is controlled by the asymmetric duty cycle and the phase shift (${\Phi}$) between the inverter lags controlled by the impedance control network. The proposed system has a capability of achieving zero voltage switching (ZVS) and zero current switching (ZCS) at all the switches over the wide range of input voltage, output voltage and output power. The feasibility of the proposed system is verified by the PSIM simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.4
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• pp.338-344
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• 2002

In this paper, neural network force tracking control is proposed. The conventional impedance function is reformulated to have direct farce tracking capability. Neural network is used to compensate for all the uncertainties such as unknown robot dynamics, unknown environment stiffness, and unknown environment position. On line training signal of farce error for neural network is formulated. A large x-y table is built as a test-bed and neural network loaming algorithm is implemented on a DSP board mounted in a PC. Experimental studies of farce tracking on unknown environment for x-y table robot are presented to confirm the performance of the proposed technique.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.17-25
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• 2014

This paper presents the formulation of an impedance controller for regulating the contact force with the environment. To achieve an accurate force tracking control, uncertainties in both robot dynamics and the environment require to be addressed. As part of the framework of the proposed force tracking formulation, a neural network is introduced at the desired trajectory to compensate for all uncertainties in an on-line manner. Compensation at the input trajectory leads to a remarkable structural advantage in that no modifications of the internal force controllers are required. Minimizing the objective function of the training signal for a neural network satisfies the desired force tracking performance. A neural network actually compensates for uncertainties at the input trajectory level in an on-line fashion. Simulation results confirm the position and force tracking abilities of a robot manipulator.

• Journal of Power Electronics
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• v.16 no.6
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• pp.2338-2349
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• 2016

This paper presents an impedance-matching-based control scheme for the harmonic resonance damping of multiple grid-connected-converters (GCCs) with LCL filters. As indicated in this paper, harmonic resonance occurs if a GCC possesses an output impedance that is not matched with the rest of the network in some specific frequency bands. It is also revealed that the resonance frequency is associated with the number of GCCs, the grid impedance and even the capacitive loads. By controlling the grid-side current instead of the converter-side current, the critical LCL filter is restricted as an internal component. Thus, the closed-loop output impedance of the GCC within the filter can be configured. The proposed scheme actively regulates the output impedance of the GCC to match the impedance of the external network, based on the detected resonance frequency. As a result, the resonance risk of multiple GCCs can be avoided, which is beneficial for the plug-and-play property of the GCCs in microgrids. Simulation and experimental results validate the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.897-899
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• 1997

The analysis of distribution line faults is essential to the proper protections of the power system. A high impedance fault does not make enough current to cause conventional protective devices. It is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. This paper describes an algorithm using back-propagation neural network for pattern recognition and detection of high impedance faults. Fractal dimensions are estimated for distinction between random noise and chaotic behavior in the power system. The fractal dimension of the line current is also used as a indication of the high impedance fault.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1844-1845
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• 2007

A CUSUM control chart was used to monitor semiconductor plasma equipment. The performance of plasma monitoring was evaluated with various combinations of design variables involved in CUCUM control chart. Experimental data collected by using a real-time matching monitoring system include electrical positions of impedance and phase positions, and reflected power. The evaluation revealed that by determining specific design variables plasma states could be more strictly monitored.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.118-121
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• 2003

In the conventional boundary estimation in EIT (Electrical Impedance Tomography), the interface between anomalies and background is expressed in usual as Fourier series and the boundary is reconstructed by obtaining the Fourier coefficients. This paper proposes a method for the boundary estimation, where the boundary of anomaly is approximated as the interpolation of front points located discretely along the boundary and is imaged by tracking the points in the region of interest. In the solution to the inverse problem to estimate the front points, the multi-layer neural network is introduced. For the verification of the proposed method, numerical experiments are conducted and the results indicate a good performance.