• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.9
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• pp.35-42
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• 1998

Finding a technique to achieve high machining precision and high productivity is an important issue for CNC machining. One of the solutions to meet better performance of machining is feedrate control. In this paper we present an adaptive feedrate neuro-control method for high precision and high speed machining. The adaptive neuro-control architecture consists of a neural network identifier(NNI) and an iterative learning control algorithm with inversion of the NNI. The NNI is an identifier for the nonlinear characteristics of feedrate and contour error, which is utilized in iterative learning for adaptive feedrate control with specified contour error tolerance. The proposed neuro-control method has been successfully evaluated for machining circular, corner and involute contours by computer simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.154-162
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• 2003

In this paper, Associative Memory with Cellular Neural Networks classifies and recognizes image patterns as an operator applied to image process. CNN processes nonlinear data in real-time like neural networks, and made by cell which communicates with each other directly through its neighbor cells as the Cellular Automata does. It is applied to the optimization problem, associative memory, pattern recognition, and computer vision. Image processing with CNN is appropriate to 2-D images, because each cell which corresponds to each pixel in the image is simultaneously processed in parallel. This paper shows the method for designing the structure of associative memory based on CNN and getting output image by choosing the most appropriate weight pattern among the whole learned weight pattern memories. Each template represents weight values between cells and updates them by learning. Hebbian rule is used for learning template weights and LMS algorithm is used for classification.

• The Transactions of the Korean Institute of Power Electronics
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• v.14 no.2
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• pp.142-149
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• 2009

This paper investigates operating characteristics of three-phase matrix converter with unity input power factor by direct duty-ratio pulse-width modulation in the case of balanced and unbalanced load. It can be found from the system analysis that (1) The control algorithm for unity power factor is not related to the variables of load sides but the input voltages, (2) With the balanced three-phase load except for the pure reactive load, the unity input power factor can be achieved, (3) In the case of the unbalanced linear load, the equivalent input characteristics of the matrix converter can be seen like the nonlinear resister, (4) When the input frequency and the output frequency have the specific relationship, each input phases have the same sharing of the average power. The feasibility and validity of the analysis were verified by simulation and experimental results.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.53-60
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• 2018

In this paper, we described the unknown parameter identification using Dual Extended Kalman Filter for precise control of 2-link manipulator. 2-link manipulator has highly non-linear characteristic with changed parameter thought tasks. The parameter kinds of mass and inertia of system is important to handle with the manipulator robustly. To solve the control problem by estimating the state and unknown parameters of the system through the proposed method. In order to verify the performance of proposed method, we simulate the implementation using Matlab and compare with results of RLS algorithm. At the results, proposed method has a better performance than those of RLS and verify the estimation performance in the parameter estimation.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.4
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• pp.38-46
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• 1999

This paper discusses the repetitive tracking control method for a coarse-fine actuator. The proposed system is composed of a magnetic linear drive as a coarse actuator and a piezoelectric linear positioner as a fine actuator. In particular, nonlinear friction in a magnetic linear drive and hysteresis characteristic of a piezoelectric linear positioner are modeled first. The feedback linearization loop uses these models in tracking position control. The control strategy is then further extended to include a repetitive control algorithm in tracking periodic reference inputs. This repetitive controller is implemented on the existing PID controller augmented with feedback linearization loop. The experimental results show that performance in tracking sinusoidal waveforms is noticeably improved by augmenting a PID controller with feedback linearization loop and a repetitive controller together.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.6
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• pp.1679-1688
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• 1990

The method of nonlinear parameter estimation(NPE), which is a statistical and an inverse method, is used to estimate the thermal diffusivity of the porous insulation material. In order to apply the NPE method for measuring the thermal diffusivity, and algorithm for programing suitable to IBM personal computer is established, and is studied the statistical treatment of experimental data and theory of estimation. The experimental data obtained by discrete measurement using a constant heat flux technique are used to find the boundary conditions, initial conditions, and the thermal diffusivity, and then the final values are compared with the values obtained by some different methods. The results are presented as follows:(1) NPE method is used to establish the estimation of the thermal diffusivity and compared results with experimental output shows, that this method can be applicable to define the thermal diffusivity without considering hear flux types. (2) Because of all of the temperatures obtained by the discrete measurement on each steps of time are used to estimate the thermal diffusivity. Although some error in the temperature measurements of temperature are included in estimating process, its influences on the final value are minimzed in NPE method. (3) NPE method can reduce the experimental time including the time of data collecting in a few minutes and can take smaller specimen compared with steady state method. If the tube-type furnace is used, also the adjusting time of surrounding temperature can be reduced.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.4
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• pp.630-648
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• 1992

The minimum weight design for simply-supported isotropic or symmetrically laminated stiffened cylindrical shells subjected to various loads (axial compression or combined loads) is studied by a nonlinear mathematical search algorithm. The minimum weight design in accomplished with the CONMIN optimizer by Vanderplaats. Several types of buckling modes with maximum allowable stresses and strains are included as constraints in the minimum weight design process, such as general buckling, panel buckling with either stingers or rings smeared out, local skin buckling, local crippling of stiffener segments, and general, panel and local skin buckling including stiffener rolling. The approach allows the consideration of various shapes of stiffening members. Rectangular, I, or T type stringers and rectangular rings are used for stiffened cylindrical shells. Several design examples are analyzed and compared with those in the previous literatures. The unstiffened glass/epoxy, graphite/epoxy(T300/5208), and graphite/epoxy aluminum honeycomb cylindrical shells and stiffened graphite/epoxy cyindrical shells under axial compression are analyzed through the present approach.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.2
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• pp.10-16
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• 2012

Currently, the majority of systems which are non-linear are in need of the correct system equations for controlling and monitoring. Therefore, the correct estimation of parameters is crucial. Generally, parameters are changed due to system deterioration or sudden environmental alterations. Given the limitations of system monitoring unstable controls can arise. In the following paper, the parameter estimation method is proposed using software filters to combat these system instabilities. For dynamic instances, a powerful particle filter is used to control the nonlinear and noisy environments in which they take place. Using a setup simulation comprised of a slider and pendulum, the state variable of noise is obtained. After collecting the data, the proposed algorithm is used to estimate both the state variable and its parameters. Finally, these results are checked with correct parameter estimations to evaluate and verify the algorithms performance.

• Journal of the Earthquake Engineering Society of Korea
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• v.15 no.6
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• pp.11-18
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• 2011

Accurate modal identification analysis is required to reasonably perform a seismic qualification of safety-related electric equipment installed in nuclear power plants (NPPs). This study evaluates a variation of the modal properties of an electric equipment cabinet structure in NPPs according to the excitation levels. For the study, an actual electric equipment cabinet was selected as a specimen and was dynamically tested by using a portable exciter in accordance with the level of input vibration energy. Tests were classified into two sets: with-door cases, and without-door cases. Frequency response functions were computed from the signals of the acceleration responses and input motions measured from the vibration tests. A polynomial curve fitting algorithm was used to extract the modal properties from the frequency response functions. This study reviews the variation of the modal properties according to the variation of the excitation levels. The results of the study show that the modal frequencies and the modal dampings of the object specimen varies nonlinearly according to the excitation level of the test motion. Attaching the door increases the modal damping of the cabinet.