This paper studies the delay-characteristics using the singular values and vectors of Hankel operators for input delay systems. First, the computational method of Hankel singular values and their corresponding singular vectors are introduced, and then it is analytically provea that all the Hankel singular vlues have a monotone increasing properties as the length of delay time increases. Furthermore, through a simple numerical example, it is shown that the Hankel singular values are dependent only on the ratio of the time constant of a lumped parameter system to the length of delay , and in case that the time constant is relatively larger than the delay time, the lumped parameter characteristic has a great influence on the input delay systems.

In this paper, three adaptive sliding mode control algorithms, which self-tune both the sliding mode gain and the boundary layer thickness, are proposed. The first algorithm uses a gain adaptation rule is combined with the boundary layer thickness adaptatioin rule to satisfy the sliding condition. In the third algorithm, the computation burden of the second algorithm is reduced further, and therefore no extra cost is required for real-time implementation. Due to the mixed sliding mode gain and the boundary layer thickness adaptation scheme, the tracking error and the chattering of the control input can be reduced greatly.

Stand-alone global position system receiver based on C/A code tracking generates position error of 100m mainly due to the selective availability and ionospheric and tropospheric delay errors. The differential GPS is the most commonly used method for removing those bias range error components. The relative GPS, although somewhat restrictive in its use, is ideally suited to the car monitoring system for improved Automatic Vehicle location, especially where the DGPS infrastructure is not available. The RGPS does not require any additional hardware, facility or external infrastructure and can be operated within the system with existing host computer and communication link. This paper presents detailed description of the RGPS concept and its implementation for real-time data processing. Performance of RGPS is evaluated with real data and is compared with DGPS.

In this paper, we design a H^\infty servo controller for gauge control of tandem cold mill. To improve the performance of the AGC(Aotomatic Gauge Control) system based on the Taylor linearized model of tandem cold mill, the H^\infty servo controller is designed to satisfy robust stability, disturbance attenuation and robust tracking properties. The H^\infty servo controller problem is modified as an usual H^\infty control problem, and the solvability condition of the H^\infty servo problem depends on the solvability of the modified H^\infty control problem. Since this modified problem does not satisfied standard assumptions for the H^\infty control problem, it is solved by an LMI(Linear Matrix Inequality) technique. Consequently, the comparison between the H^\infty servo controller and the existing PID/FF(FeedForward) controller shows the usefulness of this study.

This paper describes the design of fuzzy digital PID controller using a simplified indirect inference method. First, the fuzzy digital PID controller is derived from the conventional continuous-time linear digital PID controller,. Then the fuzzification, control-rule base, and defuzzification using SIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete-time fuzzy version of the conventional PID controller, which has the same linear structure, but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability, particularly when the process to be controlled is nonlinear. When the SIIM is applied the fuzzy inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated that the proposed method provides better control performance than the one proposed by D. Misir et al.

This paper presents the application of a FARMA controller to stabilization of nonlinear Two-Generator Five-Bus power Systems. The control rules and the membership functions of the FARMA controller are generated automatically without using any plant model high complexity and severe nonlinearity of power systems are introduced and two-Machine Five -Bus Power system stabilization problem is formulated. The simulation results demonstrate the effectiveness and application possibility of the FARMA controller to the control problem of high order and nonlinear plants.

This paper presents the characteristics of the dynamic response and calibration technique on a linear variable differential transformer(LVDT). The linear error of the LVDT was proven $\pm$1% in the static calibration and $\pm$0.5% in the dynamic calibration. In this paper, the linearity error generated in the static and dynamic state of the core movement can be eliminated using the correction algorithem of the static and dynamic state derived from the least square linear approximation for the nonlinearity of the curves of direct data fitting and Lagrange polynomials. With the static and dynamic calibration method, the calibration accuracy of the LVDT can be reduced to within $\pm{0.5%.}$.

In this paper, a model reference adaptive control for the atomic force microscope (AFM) of tapping mode is investigated. The dynamics between the AFM system and al sample is mathematically modeled as a second order spring-mass-damper system with oscillatory inputs. The attractive and repulsive forces between the tip of the AFM system and the sample are derived using the Lennard-Jones potential energy. By non-dimensionalizing the displacement of the tip and the input frequency, the chaotic behavior near a resonance frequency is better depicted through the non-dimensionalized equations. Four nonlinear analysis techniques, a phase portrait, sensitive dependence on initial conditions, a power spectral density function, and a Pomcare map are investigated. Because the equations of motion derived in this paper involve unknown parameter values such as the damping effect of the air and the interaction constants between materials, the standard model reference adaptive control is adopted. Two control objectives, the prevention of chaos and the tracking of reference signal, are pursued. Simulation results are included.

As the demand for the design of modular manipulators or special purpose manipulators has increased, task based design to design an optimal manipulator for a given task become more and more important. However, the complexity with a large number of design parameters, and highly nonlinear and implicit functions are characteristics of a general manipulator design. To achieve the goal of task based design, it is necessary to develop a methodology to solve the complexity. This paper addresses how to determine the kinematic parameters of a two-degrees of freedom manipulator with parallelogram five-bar link mechanism from a given task, namely, how to map a given task into the kinematic parameters. With simplified example of designing a manipulator with five-bar link mechanism, the methodology for task based design is presented. And it introduces formulations of a given task and manipulator specifications, and presents a new dexterity measure for manipulator design. Also the optimization problem with constraints is solved by using a genetic algorithm that provides robust search in complex spaces.

The stewart platform manipulator proposed by stewart is the parallel manipulator which is composed of several independent actuators connecting the upper plate with the base plate and capable of executing a six degree of freedom motion. The manipulator has a structure of a closed loop form, and provides better load-to-weight ratio and ratio and rigidity than a serial manipulator with an open loop form. Moreover, the manipulator has high positional accuracy because position errors of actuators are not additive. Because of these advantages, this manipulator is widely used in many engineering applications such as a driving simulator, a tool of machining center, a force/torque sensor and so on. When this Stewart platform manipulator is controlled in joint space, it is difficult to design a controller using an analytic method due to nonhnearity and unknown parameters of actuators. Therefore, a PID controller is often used because of easiness in applications. To find the PID control gain, a trial-and-error method is generally used. This method is time-consuming, and does not guarantee a optimal gain. Thus, this paper proposes a GA-PID controller which selects an optimal PID control gain using genetic algorithms. And this proposed controller is evaluated experimentally and shows acceptable performance.

Parallel manipulator is suitable for the high precise task because it than has higher stiffness, larger load capacity and more excellent precision, due to the closed-lop structure, than serial manipulator. But the controller design for parallel manipulator is difficult because the parallel manipulator has both the complexity of structure and the interference of actuators. The precision improvement of parallel manipulator using a classical linear control scheme is difficult because the parallel manipulator has the tough nonlinear characteristics. In this paper, firstly, the kinematic analysis of a parallel manipulator used at the experiments is performed so as to show the controllability. The analysis of internal singular configuration of the workspace is performed using the kinematic isotropic index so a sto show the limitation of control performance of a simple linear controller with fixed control gains. Secondly, a control scheme is designed by using an adaptive fuzzy logic controller so that active joints of the parallel manipulator track more precisely the desired input trajectory. This adaptive fuzzy logic controller so that active joints of the parallel manipulator track more precisely the desired input trajectory. This adaptive fuzzy logic controller is often used for the control of nonlinear system because it has both the inference ability and the learning ability. Lastly, the effeciency of designed control scheme is demonstrated by the real-time control experiments with IBM PC interface logic H/W and S/W of my won making. The experimental results was a success.

In this paper, the illuminator is designed to recognize the shape and the existence of holes of radiation fin in the point that the light reflection characteristics are different according to the roughness of the material. The threshold value, the positions of holes and the black pixel nembers in the positon are obtained under the illuminator, in accordance with the reference image, by applying binary conversion and hole segmentation algorithm, as they are suggested in this paper, The existence and shape of hole are recognized by calculating the distance and feature value in the test image, which is obtained from the parameters of reference image. It is programmed to apply to GUI(Graphic User the Interface) in windows. More than 98% of recognition rate is shown, as it is applied to three different sizes of the radiation fin.

This paper suggests a method to recognize the various defect patterns of a cold mill strip using a binary decision tree automatically constructed by a genetic algorithm(GA). In classifying complex patterns with high similarity like the defect patterns of a cold mill stirp, the selection of an optimal feature set and an appropriate recognizer is important to achieve high recognition rate. In this paper a GA is used to select a subset of the suitable features at each node in the binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes using a linear decision function. This process is repeated at each node until all the patterns are classified into individual classes. In this way, the classifier using the binary decision tree is constructed automatically. After constructing the binary decision tree, the final recognizer is accomplished by having neural network learning sits of standard patterns at each node. In this paper, the classifier using the binary decision tree is applied to the recognition of defect patterns of a cold mill strip, and the experimental results are given to demonstrate the usefulness of the proposed scheme.

A fuzzy logic Run-by-Run(RbR) controller and an in -line wafer characteristics prediction scheme for the rapid thermal processing system have been developed for the study of process repeatability. The fuzzy logic RbR controller provides a framework for controlling a process which is subject to disturbances such as shifts and drifts as a normal part of its operation. The fuzzy logic RbR controller combines the advantages of both fuzzy logic and feedback control. It has two components : fuzzy logic diagnostic system and model modification system. At first, a neural network model is constructed with the I/O data collected during the designed experiments. The wafer state after each run is assessed by the fuzzy logic diagnostic system with featuring step. The model modification system updates the existing neural network process model in case of process shift or drift, and then select a new recipe based on the updated model using genetic algorithm. After this procedure, wafer characteristics are predicted from the in-line wafer characteristics prediction model with principal component analysis. The fuzzy logic RbR controller has been applied to the control of Titanium SALICIDE process. After completing all of the above, it follows that: 1) the fuzzy logic RbR controller can compensate the process draft, and 2) the in-line wafer characteristics prediction scheme can reduce the measurement cost and time.

A die-casting junction method is usually used to manufacture the tub of an IH(induction heating) jar. If there is a very small air bubble in the junction area, the thermal conductivity is deteriorated and local overheat occurs. Such problem brings serious inferiority of the IH jar. In this paper, we propose a new method to detect such defect with simply measured thermal data. Thermal distribution of preheated tubs is obtained by scanning with infrared thermal sensors and analyzed with the statistic pattern recognition method. By defining the characteristic feature as the temperature difference between sensors and using ellipsoid function as decision boundary, a supervised learning method of genetic algorithm is proposed to obtain the required parpameters. After applying the proposed method to experiment, we have proved that the rate of recognition is high even for a small number of data set.