Transactions of the Institute of Systems, Control and Information Engineers
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ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668

Transactions of the Institute of Systems, Control and Information Engineers Vol. 13 (2000), No. 9

  • Direct Design of a Model Following Anti-Windup Control System Based on Youla Parametrization

    pp. 395-402

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    DOI:10.5687/iscie.13.9_395

    In this paper, we consider the anti-windup control system design based on Youla parametrization. We derive sufficient conditions for the stability and the model following performance of the present anti-windup control system in terms of scaled H conditions. Furthermore, based on these conditions, we provide a direct design method of the anti-windup controller achieving the good model following performance even when the control input is saturated. A numerical example shows the applicability of the proposed design method.
  • A Design Method for Nonlinear H Control Based on Hamilton-Jacobi-Isaacs Equation

    pp. 403-412

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    DOI:10.5687/iscie.13.9_403

    Based on Hamilton-Jacobi-Isaacs equation, we deal with a design problem of the state feedback controller for the nonlinear H control problem. Making use of the emergent property of Genetic Programming, we try to approximately obtain the solution of the Hamilton-Jacobi-Isaacs equation. By introducing the automatic differentiation technique, we then combine the Genetic Programming with the nonlinear H control problem. Some numerical examples are demonstrated to illustrate the efficiency of the proposed design method.
  • A Sequencing Domain Shell for a Mixed-Model Assembly Line

    pp. 413-423

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    DOI:10.5687/iscie.13.9_413

    This paper proposes a sequencing domain shell for a mixed-model assembly line and also discusses the application result from a practical production of a bumper coating line. The domain shell, SELES (SELEction tool for production instruction Scheduling), was developed to construct sequencing expert systems individually and provided the functions to select an appropriate product from plural candidates with respect to constraints such as line situation and line balancing. The characteristics of SELES are as follows : (1) the meta-inference mechanism and knowledge representation enable to straightforwardly describe flows of sequencing and maintain easily knowledge bases, (2) the selection mechanism using filtering primitives is understandable and widely applicable for various kinds of production lines. We applied SELES to a sequencing system for a practical bumper coating line. As a result, we could reduce a mount of labors dramatically and decrease a stock space of products in some production processes.
  • Observer Based Partial Switching Control of Systems with State and Control Constraints

    pp. 424-432

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    DOI:10.5687/iscie.13.9_424

    This paper considers output-feedback switching control of systems with pointwise-in-time state and control constraints. In the case of full state feedback, the ability of switching control laws for resolving a common conflict in the design of state regulators has been proved. Under measurement feedback, previous work of the authors derived the switching control law utilizing a set-valued state estimation technique. However, the online computational burden in constructing the set-valued estimates of the plant state may severely limit its applicability. Proposed methods in this paper alleviate this burden. The main idea is to construct an upper bound of certain quadratic form of unmeasured error signals. The resulting measurement feedback switching control law is then constructed in conjunction with this upper bound as an estimation error, rather than set-valued estimate of the state.
  • A Robust Fuzzy Classifier with Ellipsoidal Regions

    pp. 433-440

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    DOI:10.5687/iscie.13.9_433

    In this paper, we discuss a robust training method for a fuzzy classifier with ellipsoidal regions. First, we define a fuzzy rule for each class. Next, we determine the weight for each training datum Dy the two-stage method in order to suppress the effect of outliers. Then, using these weights, we calculate the center and covariance matrix of the ellipsoidal region for each class and tune the Fuzzy rules. After tuning, to further improve generalization ability, we tune fuzzy rules between two classes using the training data in the class boundary. We demonstrate the effectiveness of our method using four benchmark data sets.

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