The control effectiveness and disturbance suppressibility are mainly governed by a left eigenstructure of a system. In this paper, a control algorithm which uses an output feedback eigenstructure assignment scheme is proposed in order that a desired closed-loop system has the specified degree of controllability and/or degree of disturbance suppressibility. To do this, a modal and a gross disturbance suppressibility measures are proposed. A modified version of Hamdan and Nayfeh's modal controllability measure is also presented. The validity and usefulness of the proposed measures and the controller design algorithm are illustrated by designing a controller for a third-order system as an example.

In this paper, we consider the robustness analysis problem in state space models with linear time invariant perturbations. Based upon the discrete-time Lyapunov approach, sufficient conditions are derived for the eigenvalues of perturbed matrix to be located in a circle, and robustness bounds on perturbations are obtained. Spaecially, for the case of a diagonalizable hermitian matrix the bound is given in terms of the nominal matrix without the solution of Lyapunov equation. This robustness analysis takes account not only of stability robustness but also of certain types of performance robustness. For two perturbation classes resulting bounds are shown to be improved over the existing ones. Examples given include comparison of the proposed analysis method with existing one.

본 논문에서는 이산시간 LQ 조절기의 안정도 강인성을 주파수 영역및 시간영역에서 고찰하고 그 향상책을 제시하낟. 주파수영역에서 강인성 척도인 궤환차행렬(return difference matrix) 의 최소특이치가 상태가중치 행렬과 제어가중치 행렬의 비와 반비례함을 보이고, 시간영역에서 매개변수의 변화에 대한 안정도 강인성 범위들을 얻는다. 이 범위들의 점근적 성질을 밝히기 위하여 LQ 궤환이득의 특이치들이 상태가중치 행렬과 제어기중치 행렬의 비의 증가함수 임을 보인다. 몇가지 조건하에서 시스템 행렬(입력행렬)에 대한 안정도 강인성 범위가 상태 가중치 행렬과 제어가중치 행렬의 비가 증가(감소)함에 따라서 증가함을 보이고, 이러한 사실들을 예제를 통하여 검증한다.

This paper presents a study using neural networks in the design of the tracking controller of robotic systems. Our strategy is to put to use the available knowledge about the robot manipulator, such as estimation models, in the contoller design via the computed torque method, and then to add the neural network to control the remaining uncertainty. The neural network used here learns to provide the inverse dynamics of the plant uncertainty, and acts as an inverse controller. In the simulation study, we verify that the proposed neural network controller is robust not only to structured uncertainties, but also to unstructured uncertainties such as friction models.

This paper proposes an optimal design method for the weight and cost of a moving magnet typer linear DC motor (MM-LDM). The optimal design condition such as type and size of MM-LDM were determined by the trinary search algorithm after adjusting a standard function and its related parameters. In order to verigy results of the optimal design by the computer simulation, the designed values such as a thrust, a current, a velocity, and etc. of the fabricated MM-LDM were measured. And the measurement results are in good agreement with the designed ones.

For the robust control, sliding mode control has gained a great attention. Sliding mode control has the good robustness, because it makes the state of system reach the origin of the state space, by a varying the structure of system on the sliding surface. The slope of sliding surface affects to the control performance. If it is small, robustness is increased at the expense of reaching time. On the contrary, if it is large, reaching time is decreased at the expense of robustness and overshoot. In this paper, to design the optimal sliding surface, optimal control theory is introduced. To confirm the validity of the proposed method, the position control of step motor is implemented.

This paper proposes a segmentation algorithm using gray-level discontinuity and surface reflectance ratio of input images obtained under different illumination conditions. Each image is divided by a certain number of subregions based on the thresholds. The thresholds are determined using the histogram of fusion image which is obtained by ANDing the multiple input images. The subregions of images are projected on the eigenspace where their bases are the major eigenvectors of image matrix. Points in the eigenspace are classified into two clusters. Images associated with the bigger cluster are fused by revised ANDing to form a combined edge image. Missing edges are detected using surface reflectance ration and chain code. The proposed algorithm obtains more accurate edge information and allows to more efficiently recognize the environment under various illumination conditions.

In designing assembly lines, it is required that the lines should not only meet the demand of the product, but also minimize the assembly cost associated with the line. For such a purpose, numerous research efforts have been made on either the assembly sequence generation or the assembly line balancing. However, the works dealing with both the research problems have been seldom reported in literature. When assembly sequences are generated without consideration of line balancing, additional cost may be incurred, because the sequences may not guarantee the minimum number of workstations. Therefore, it is essential to consider line balancing in the generation of cost-effective assembly sequences. To incorporate the two research problems into one, this paper treats a single-model and deterministic (SMD) assembly line balancing (ALB) problem, and proposes a new method for generating line-balanced robotic assembly sequences by using a simulated annealing. In this method, an energy function is derived in consideration of the satisfaction of assembly constraints, and the minimization of both the assembly cost and the idle time. Then, the energy function is iteratively minimized and occasionally perturbed by the simulated annealing. When no further change in energy occurs, an assembly sequence with consideration of line balancing is finally found. To show the effectiveness of the proposed scheme, a case study for an electrical relay is presented.

The purpose of this study is to develop the technique of energy recovery and energy saving by using the optimization of heat exchanger network synthesis. This article proposes a new method of determining the optimal target of a heat exchanger network synthesis problem of which data feature multiple pinch points. The system separation method we suggest here is to subdivide the original system into independent subsystems with one pinch point. The optimal cost target was evaluated and the original pinch rules at each subsystem were employed. The software developed in this study was applied to the Alko prosess, which is an alcohol production process, for the synthesis of heat exchanger network. It was possible to save about 15% of the total annual cost.

For use in patients with severe forms of heart disease for which no surgical repair is possible, development of artificial hearts has many importance in point of economics, medical and industrial applications. To provide a sufficient cardiac output to the physiological demands of circulatory systems is the objective of control systems for an electromechanical artificial heart, which is based on the stable controller design for the motor in the artificial heart. In this paper, an implantable microcontroller-based brushless DC motor control system with the implantability, reliability, and stability is introduced. The developed control system for the artificial heart has the following advantages: (1) It is possible to be implanted in a body by realizing the fundamental functions such as a motor speed detection, proportional-intergral control, timer, and PWM generation through a software programming. (2) Thus, the power consumed in the controller is reduced. (3) The reliability and stability are improved through the reduction of electronic parts and line connetions at the controller. The performance of the artificial hearts and control system developed was evaluated through a series of mock circulatory experiments and a reliability test for one and half years. A sheep with the artificial heart and control system was survived for three days.