In this paper, the decoupling H(sub)$\infty$ controller which minimizes the maximum energy in the output signal is designed to reduce the coupling properties between the input/output variables which make it difficult to control a system efficiently. The state-space formulas corresponding to the existing transfer matrix formulas of the controller are derived for computational efficiency. And for a given decoupling $H_{\infty}$ problem, an efficient method are sought to find the controller coefficients through the LMI(Linear Matrix Inequalities) method by which the problem is formulated into a convex optimization problem.

This paper presents a design method of sliding model control (SMC) for single input linear systems with mismatched uncertainties. We define a virtual state based on the controllable canonical form of the nominal system. And we defined a sliding surface for the augmented system with a virtual state. This sliding surface makes it possible to use the SMC technique with various types of controllers. In this paper, we construct a controller that combines SMC with robust controller. We design a robust controller for the system with mismatched uncertainties using a form of linear matrix inequality(LMI). We make a virtual state from this robust control input and the states of the nominal system. And we design a sliding model controller that stabilizes the overall closed-loop system.

In this paper, vibration suppression of an elastic beam fixed on a moving cart and carrying a fixed or moving mass is considered. A modified pulse sequence method with RIC(Robust Internal-loop Compensator) is proposed to suppress the single model residual vibration and to get accurate positioning of the beam-mass-cart system. The performance of the proposed input preshaping method is compared with that of the previous ones through simulations and experiments. Using the proposed method, it is able to suppress the initial vibration of the beam-mass-cart system carrying a concentrated mass. Accurate PTP(point-to-point) positioning of the moving mass without residual vibration is also obtained experimentally by modifying the proposed pulse sequence method. Finally, the proposed input preshaping method is applied successfully to the system to follow square trajectories of the moving mass without residual vibration.

In this paper, we propose a linear quadratic regulator(LQR) controller design for the active suspension using evolution strategy(ES) and neural network. We can improve the inherent suspension problem, the trade-off between ride quality and suspension travel by selecting appropriate weight in the LQR-objective function. Since any definite rules for selecting weights do not exist, we replace the designers trial-and-error method with ES that is an optimization algorithm. Using the ES, we can find the proper control gains for selected frequencies, which have major effects on the vibrations of the vehicle. The relationship between the frequencies and proper control gains are generalized by use of the neural networks. When the vehicle is driven, the trained neural network is activated and provides the proper gains for operating frequencies. And we adopted double sky-hook control to protect car component when passing large bump. Effectiveness of our design has been shown compared to the conventional sky-hook controller through simulation studies.

The switched reluctance motor(SRM) has been increasingly used in high-performance servo applications such as electric vehicles, aircraft, and direct-drive robots. The dynamic equations of SRMs are, however, highly nonlinear and this makes it difficult to control SRMs with high performance. In this paper, we propose a new robust current tracking controller for SAMs which can compensate the nonlinear characteristics of SRM(i.e., back-emf and inductance) completely and hence shows perfect tracking performance even with an arbitrary small current control loop gain. Furthermore, even in case that there exist some model uncertainties, our current controller guarantees that the stator currents can track the reference current commands with sufficiently small tracking errors. In order to justify our work, we present the tracking performance analysis and some simulation results.

In this paper, we propose two design methods of neural networks controller for the height control of helicopter, one is the design of neural network controller having learning capability and the other is the design of more improved neural network controller. Through the simulation results, we show that the proposed controllers have controllers have enhanced control performance(rapid response, effectiveness and safety) than the typical neural networks controller in the height control of helicopter.

This paper describes a method for detecting a close leading vehicle using the contour of the vehi-cles rear wheels. The contour of a leading vehicles rear wheels in 속 front road image from a B/W CCD camera mounted on the central front bumper of the vehicle, has vertical components and can be discerned clearly in contrast to the road surface. After extracting positive edges and negative edges using the Sobel op-erator in the raw image, every point that can be recognized as a feature of the contour of the leading vehicle wheel is determined. This process can detect the presence of a close leading vehicle, and it is also possible to calculate the distance to the leading vehicle and the lateral deviation angle. This method might be useful for developing and LSA (Low Speed Automation) system that can relieve drivers stress in the stop-and-go traffic conditions encoun-tered on urban roads.

In this paper, we described an approach to the automation of visual inspection of BGA solder joint defects of surface mounted components on printed circuit board by using neural network. Inherently, the BGA solder joints are located underneath its own package body, and this induces a difficulty of taking good image of the solder joints by using conventional imaging systems. To acquire the cross-sectional image of BGA sol-der joint, X-ray cross-sectional imaging method such as laminography and digital tomosynthesis has been cur-rently utilized. However, the cross-sectional image obtained by using laminography or DT methods, has inher-ent blurring effect and artifact. This problem has been a major obstacle to extract suitable features for classifi-cation. To solve this problem, a neural network based classification method is proposed int his paper. The per-formance of the proposed approach is tested on numerous samples of printed circuit boards and compared with that of human inspector. Experimental results reveal that the method provides satisfactory perform-ance and practical usefulness in BGA solder joint inspection.

According to the advancement of web related technologies, many works on robots using these technologies, called web-based robots enables sharing of expensive equipments as well as control of remote robots. But none of the existing methods about web-based robots in-clude robot simulators in their web browser, which transfer appropriate information of a remote place to the local users. In this paper, a web-based robot simulator is proposed and developed to control a remote robot by using the web. The proposed simulator can transfer the 3D information about the remote robot to the local users by using 3D graphics, which has not been previously developed. Also, it sends the camera image of a remote place to the local users so that the users can use this camera image as well as 3D information in order to control the remote robot.

A new control scheme s proposed for the control of reactive batch distillation (RBD) column. A nonlinear wave model captures the essential dynamic behavior of the RBD process. The proposed control scheme is based on both Generic Model Control(GNC) and nonlinear wave model. The control scheme uses a profile position of the column as a controlled variable. Ethanol esterification process using RBD is chosen as an example process. Tight control of the distillate purity is obtained with the use of the proposed control scheme.

In land navigation applications, two kinds of GPS/DR integration schemes are commonly used; the loosely-coupled integration scheme and the tightly-coupled one. The loosely-coupled integration filter has a simple structure and is easy to implement. When the number of visible satellites is insufficient, however, it cannot calibrate the errors of the DR sensors. On the contrary the tigthly-coupled integration filter can sup-press the growth of the error in the DR output even when the visibility is poor. However, it has larger com-putation load due to the state dimension and is inconsistent because of the variation in the measurement dimension. This paper presents a GPS/DR integration scheme with dual integration mode. During when the number of visible satellites is sufficient, the proposed scheme operates in a loosely-coupled integration mode. When the visibility becomes poor, it is switched into a tightly-coupled integration mode. Consequently, the pro-posed scheme can calibrate the DR sensors even when the visibility is poor. In addition, its computation time remains constant even if the number of visible satellites increases. Field experiment results show that the performance of the proposed integration method is almost similar to that of the tightly-coupled one.

Many industrial processes that are operated by rotating machines and large air-moving fans are excellent examples to which the single channel ANC systems can be applied. In these environments, the active noise control techniques are most popular nowadays. In this paper, a modified LMS algorithm(EAC, Error Amplitude Compared) is proposed. The algorithm is a kind of variable step-size LMS-type algorithm. Computer simulations show that the proposed EAC algorithm achieves a better convergence time than a conventional VS(Variable Step-Size) algorithm, Also, this algorithm has been implemented by using and experimental duct system.