• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.3
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• pp.16-24
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• 1992

In this paper, we solve the problem of designing a robust optimal controller which minimizes the H$\infty$-norm of the mixed sensitivity function matrix for linear multivariable systems. For a given minimized value, ${\gamma}$>o, an algorithm of finding all stabilizing controllers, such that the H$\infty$-norm of the mixed sensitivity function matrix is less than ${\gamma}$, is developed. The proposed algorithm, which is based on the model-matching and the interpolation theory, can be used for the H$\infty$-optimization problem.

• Communications of the Korean Mathematical Society
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• v.28 no.4
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• pp.783-797
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• 2013

Recently, Liu et al. [Bilateral generating functions for the Chan-Chyan-Srivastava polynomials and the generalized Lauricella function, Integral Transform Spec. Funct. 23 (2012), no. 7, 539-549] investigated, in several interesting papers, some various families of bilateral generating functions involving the Chan-Chyan-Srivastava polynomials. The aim of this present paper is to obtain some bilateral generating functions involving the Chan-Chyan-Sriavastava polynomials and three general classes of multivariable polynomials introduced earlier by Srivastava in [A contour integral involving Fox's H-function, Indian J. Math. 14 (1972), 1-6], [A multilinear generating function for the Konhauser sets of biorthogonal polynomials suggested by the Laguerre polynomials, Pacific J. Math. 117 (1985), 183-191] and by Kaano$\breve{g}$lu and $\ddot{O}$zarslan in [Two-sided generating functions for certain class of r-variable polynomials, Mathematical and Computer Modelling 54 (2011), 625-631]. Special cases involving the (Srivastava-Daoust) generalized Lauricella functions are also given.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1551-1558
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• 2013

$H_2$ design of decoupled control systems is treated in the generalized plant model. The existence condition of a decoupling controller is stated and a parameterized form of all achievable decoupled closed loop transfer matrices is presented by using the directional interpolation approaches under the assumption of simple transmission zeros. The class of all decoupling controllers that yield finite cost function is obtained as a parameterized form and an illustrative example to find the optimal controller is provided.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.4
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• pp.44-55
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• 2004

In order to meet the requirement for higher thickness accuracy in tandem cold rolling processes, it is strongly necessary to have good performance in control units. To meet this requirement, this paper suggested an output regulating control system with a roll-eccentricity estimator for each rolling stand of tandem cold mills. Considering entry thickness variation and roll eccentricity simultaneously as the major disturbances, a synthesis of multivariable control systems was presented based on H$\infty$ control theory, which could reflect the knowledge of input direction and spectrum of disturbance signals on design. Then, to effectively reject roll eccentricity, a weight function having some poles on the imaginary axis was introduced. This lead to a non-standard H$\infty$ control problem, and the design procedures for solving this problem were analytically presented. The effectiveness of the proposed control method was evaluated through computer simulations and compared to that of the conventional linear quardratic control and feedforward control methods for roll eccentricity.

• Journal of Mechanical Science and Technology
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• v.18 no.1
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• pp.45-54
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• 2004

In order to meet the requirement for higher thickness accuracy in cold rolling processes, it is strongly desired to have high performance in control units. To meet this requirement, we have considered an output regulating control system with a roll-eccentricity estimator for each rolling stand of tandem cold mills. Considering entry thickness variation as well as roll eccentricity as the major disturbances, a synthesis of multivariable control systems is presented based on H$\sub$$\infty$/ control theory, which can reflect the knowledge of input direction and spectrum of disturbance signals on the design. Then, to reject roll eccentricity effectively, a weight function having some poles on the imaginary axis is introduced. This leads to a non-standard H_ control problem, and the design procedures for solving this problem are analytically presented. The effectiveness of the proposed control method is evaluated through computer simulations and compared to that of the conventional LQ control and feedforward control methods for roll eccentricity.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.150-150
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• 2000

We as modal parameter estimation technique by developing a residual based system reconstruction and using the system matrix coordinate transformation. The modal parameters can be estimated from and residues of the system transfer functions expressed in modal coordinate basis, derived from the state space system matrices. However, for modal parameter estimation of multivariable and order structural systems over broad frequency bands, this non-iterative algorithm gives high accuracy in the natural fre- and damping ratios. From vibration tests on cross-ply and angle-ply composite laminates, the natural frequencies and damping ratios on be estimated using tile coordinates of the structural system reconstructed fro the experimental frequency response. These results are compared with those of finite element analysis and single-degree-of-freedom curve-fitting.

• Transactions of Materials Processing
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• v.32 no.2
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• pp.74-80
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• 2023

A deformation behavior of commercially pure titanium (CP-Ti) is highly dependent on material and processing parameters, such as deformation temperature, deformation direction, and strain rate. This study aims to predict the multivariable and nonlinear tensile behavior of CP-Ti using machine learning based on three algorithms: artificial neural network (ANN), light gradient boosting machine (LGBM), and long short-term memory (LSTM). The predictivity for tensile behaviors at the cryogenic temperature was lower than those in the room temperature due to the larger data scattering in the train dataset used in the machine learning. Although LGBM showed the lowest value of root mean squared error, it was not the best strategy owing to the overfitting and step-function morphology different from the actual data. LSTM performed the best as it effectively learned the continuous characteristics of a flow curve as well as it spent the reduced time for machine learning, even without sufficient database and hyperparameter tuning.