This paper deals with the design of delay-dependent $H_{\infty}$ control for time-delayed linear systems. We propose the two different model transformations to handle efficiently the time delay and derive conditions linear matrix inequalities (LMI's) of which are dependent on the size of time-delay and its time-derivative. The $H_{\infty}$ controller is obtained by checking the feasibility of these LMI's. Finally, we show the usefulness and applicability of our results by an example.

The brushless DC motor(BLDCM) is widely used in many applications. One of the application of the BLDCM is the stage which is one of process in the semiconductor manufacturing processes. Very high performance is required in the stage process. In this paper, the 1 degree of freedom positioning system for the basic technology of the stage is studied. The linearization method is proposed to make the controller design procedures easy by measuring the thrust force ripple using the strain gauge. And through the experiments, it is proved that the inner velocity control loop is necessary to make more precise positioning control system.

This paper proposes adaptive impedance control algorithm using fuzzy inference when robot contacts with its environments. The characteristics of the adaptive impedance controller is to adapt with parametric uncertainty and nonlinear conditions. The control algorithm is to join impedance controller with fuzzy inference engine. The proposed control method overcomes the problem of impedance controller using gain-tuning algorithm of fuzzy inference engine. We implemented an experimental set-up consisting of environment-generated one-link robot system and DSP system for controller development. We apply the adaptive fuzzy impedance controller to one-link root system, and it shows the good performance on regulating the interactive force in case of contacting with arbitrary environment.

In this study, we introduce the adaptive Polynomial Neuro-Fuzzy Networks(PNFN) architecture generated from the fusion of fuzzy inference system and PNN algorithm. The PNFN dwells on the ideas of fuzzy rule-based computing and neural networks. Fuzzy inference system is applied in the 1st layer of PNFN and PNN algorithm is employed in the 2nd layer or higher. From these the multilayer structure of the PNFN is constructed. In order words, in the Fuzzy Inference System(FIS) used in the nodes of the 1st layer of PNFN, either the simplified or regression polynomial inference method is utilized. And as the premise part of the rules, both triangular and Gaussian like membership function are studied. In the 2nd layer or higher, PNN based on GMDH and regression polynomial is generated in a dynamic way, unlike in the case of the popular multilayer perceptron structure. That is, the PNN is an analytic technique for identifying nonlinear relationships between system's inputs and outputs and is a flexible network structure constructed through the successive generation of layers from nodes represented in partial descriptions of I/O relatio of data. The experiment part of the study involves representative time series such as Box-Jenkins gas furnace data used across various neurofuzzy systems and a comparative analysis is included as well.

This paper considers the design technique of the real-time control algorithm to implement the electronic interlocking system which is the most important station control system in railway signal field. The proposed technique consists of the structure design and the detail design which are based on the ROOM(Real-Time Object-Oriented Modeling). The structure design is designed with a modeling using the heuristic search technique which, at first, catch and make out the specific requested condition, and then, is designed on the requested condition. The detail design can be implemented if it may get the satisfying values through the repetitive modeling after comparing and examining the data obtained from the structure design in order for the more reliable and accurate system to be implemented. The technique proposed in this paper is implemented with C++ language which is easy to be transferred and compatible with the existing interfaces, and also the operating system is designed and simulated on the VRTX which is a real-time operating system. This proposed technique is applied to the typical station model in order to prove the validity as verifying the performance of the modeled station.

In this paper, a new auto-focusing algorithm for digital cameras is proposed. One of the primary concerns of digital image processing is to increase image quality, and the most important factor for degrading the images is the blurring effect due to inexact focusing. The blurring effect occurs when the focusing lens is located on an unsuitable position. Therefore, focusing on an object should be proceeded before acquiring images. The proposed auto-focusing algorithm is MMDT(min-max difference threshold), and the performance of the proposed algorithm is evaluated by the use of the focus curve. It is shown that the proposed algorithm is superior to other previous auto-focusing algorithms in both the focus shape and computation time aspects. Especially, the improvement of the focus curve shape in both monotonousness and slope indicates that focusing can be done rapidly in comparison with other previous proposed algorithms.