• Proceedings of the KIPE Conference
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• 2002.11a
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• pp.49-52
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• 2002

This paper presents neural load torque observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• Proceedings of the KIPE Conference
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• 2003.07a
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• pp.393-397
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• 2003

This paper presents neural load torque observer tha used to deadbeat load torque observer and regulation of the compensation gun by parameter estimator. As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator li combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.193-196
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• 1997

This paper presents the Fault Pattern Estimator(FPE) using the neural network for the protection of the T/L. The proposed FPE has two neural network parts of the fault-types classification and the fault-location estimation. It can detect the fault signals more Quickly and accurately. To prove the performance of the FPE, we have tested using a relaying signals obtained from the EMTP simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2108-2116
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• 2008

The paper concerns Fuzzy C-Means clustering based Radial Basis Function neural networks (FCM-RBFNN) and the optimization of the network is carried out by means of Particle Swarm Optimization(PSO). FCM-RBFNN is the extended architecture of Radial Basis Function Neural Network(RBFNN). In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM - RBFNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Weighted Least Square Estimator(WLSE) are used to estimates the coefficients of polynomial. Since the performance of FCM-RBFNN is affected by some parameters of FCM-RBFNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the PSO is exploited to carry out the structural as well as parametric optimization of FCM-RBFNN. Moreover The proposed model is demonstrated with the use of numerical example and gas furnace data set.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.339-341
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• 2006

This paper presents a new method on controller design of brushless dc motors. In such drives the current ripples are generated by motor inductance in stator windings and the back EMF. To suppress the current ripples the current controller is generally used. To minimize the size and the cost of the drives it is desirable to control motors without the current controller and the current sensing circuits. To estimate the motor current it is modeled by a neural network that is configured as an output-error dynamic system. The identified model is essentially a one step ahead prediction structure in which fast inputs and outputs are used to calculate the current output. Using the model, effective estimator to compensate the effects of disturbance has been designed. The effectiveness of the proposed current estimator is verified through experiments.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.130-136
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• 2021

In recent years, much study has been conducted in robotic grasping. The grasping algorithms based on deep learning have shown better grasping performance than the traditional ones. However, deep learning-based algorithms require a lot of data and time for training. In this study, a grasping algorithm using an artificial neural network-based graspability estimator is proposed. This graspability estimator can be trained with a small number of data by using a neural network based on the residual blocks and point clouds containing the shapes of objects, not RGB images containing various features. The trained graspability estimator can measures graspability of objects and choose the best one to grasp. It was experimentally shown that the proposed algorithm has a success rate of 90% and a cycle time of 12 sec for one grasp, which indicates that it is an efficient grasping algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.69-76
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• 2008

This paper concerns Fuzzy Radial Basis Function Neural Network (FRBFNN) and automatic rule generation of extraction of the FRBFNN by means of mountain clustering. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values (degree of membership) directly rely on the computation of the relevant distance between data points. Also, we consider high-order polynomial as the consequent part of fuzzy rules which represent input-output characteristic of sup-space. The number of clusters and the centers of clusters are automatically generated by using mountain clustering method based on the density of data. The centers of cluster which are obtained by using mountain clustering are used to determine a degree of membership and weighted least square estimator (WLSE) is adopted to estimate the coefficients of the consequent polynomial of fuzzy rules. The effectiveness of the proposed model have been investigated and analyzed in detail for the representative nonlinear function.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.90-102
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• 2009

In this paper, a tracking control problem for a mechanical servo system with nonlinear dynamic friction is treated. The nonlinear friction model contains directly immeasurable friction state and the uncertainty caused by incomplete modeling and variations of its parameter. In order to provide the efficient solution to these control problems, we propose a hybrid control scheme, which consists of a robust friction state observer, a RFNN estimator and an approximation error estimator with sliding mode control. A sliding mode controller and a robust friction state observer is firstly designed to estimate the unknown infernal state of the LuGre friction model. Next, a RFNN estimator is introduced to approximate the unknown lumped friction uncertainty. Finally, an adaptive approximation error estimator is designed to compensate the approximation error of the RFNN estimator. Some simulations and experiments on the mechanical servo system composed of ball-screw and DC servo motor are presented. Results demonstrate the remarkable performance of the proposed control scheme.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.8
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• pp.1907-1914
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• 1995

An on-line measurement of the workpiece hardened depth in laser surface hardening processes is very much difficult to achieve, since the hardening process occurs in depth wise direction. In this paper, the hardened depth is estimated using a multilayered neural network. Input data of the neural network are the surface temperatures at arbitrary chosen five surface points, laser power and traveling speed of laser beam torch. To simulate the actual hardening process, a finite difference method(FDM) is used to model the process. Since this model yields the calculation results of the temperature distribution around the workpiece volume in the vicinity of the laser torch, this model is used to obtain the network's training data and laser to evaluate the performance of the neural network estimator. The simulation results show that the proposed scheme can be used to estimate the hardened depth with reasonable accuracy.

• Proceedings of the KWS Conference
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• 1993.05a
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• pp.115-119
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• 1993

Many qualitive analyses of sampled process variables have been attempted to predict nugget size in resistance spot welding process. In this paper, dynamic resistance and electrode movement signal which is a good indicative of the nugget size was examined by introducing an artificial neural network estimator. An artificial neural feedforward network with back-propagation of error was applied for the estimation of the nugget size. To assess the advantage of this method. results have been compared with conventional regression method.