• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.305-307
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• 1992

This paper presents a technique, which identifies both process noise covariance and sensor noise covariance by using innovation correlation test. A correlation test, which checks whether the square root Kalman filter is workingly optimal or not, is given. The system is stochastic autoregressive moving-average model with auxiliary white noise Input. The linear quadratic Gaussian control is used for minimizing stochastic cost function. This paper indentifies Q, R, and estimates parametric matrics $A(q^{-1}),B(q^{-1}),C(q^{-1})$ by means of extended recursive least squares and model reference control. And The proposed technique has been validated in simulation results on the fourth order system.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.169-172
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• 1999

In this paper, piecewise quadratic Lyapunov functions are used to analyze the stability of fuzzy-model-based controller. We represent the nonlinear system using a Takagi-Sugeno fuzzy model, which represent the given nonlinear system by fuzzy inference rules and local linear dynamic models. The proposed stability analysis technique is developed by dividing the whole fuzzy system into the smaller separate fuzry systems to reduce the conservatism. Some necessary and sufficient conditions for the proposed method are obtained. Finally, stability of the closed system with various kinds of controller for TS fuzzy model is checked through the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.148-157
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• 1996

Availability of input trajectories corresponding to desired output trajectories is often important in designing control systems for batch and other transient processes. In this paper, we propose a predictive control-type model-based iterative learning algorithm which is applicable to finding the nominal input trajectories of a linear time-invariant batch process. Unlike the other existing learning control algorithms, the proposed algorithm can be applied to nonsquare systems and has an ability to adjust noise sensitivity as well as convergence rate. A simple model identification technique with which performance of the proposed learning algorithm can be significantly enhanced is also proposed. Performance of the proposed learning algorithm is demonstrated through numerical simulations.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.8
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• pp.376-383
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• 2001

Factors leading to poor control of the steam generator in a nuclear power plant are nonminimum phase characteristics, unreliable of flow measurements and nonlinear characteristics, which increase more at low power(below 20%) operation. And the study of problems for water level control in the steam generator is that design water level controller only power renge, not entire. This paper introduces a model predictive control(MPC) algorithm for solving poor control factors and quadratic programming(QP) for solving input constraints. Also presents the design method of stable model predictive controller in the entire power range. The simulation results show the efficiency of proposed MPC controller by comparing with PI controller, and effect of the design parameters.

• Communications for Statistical Applications and Methods
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• v.25 no.3
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• pp.275-282
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• 2018

This paper discusses the use of projections for the sums of squares in the analyses of variance for two-way nested design data. The model for this data is assumed to only have random effects. Two different sizes of experimental units are required for a given experimental situation, since nesting is assumed to occur both in the treatment structure and in the design structure. So, variance components are coming from the sources of random effects of treatment factors and error terms in different sizes of experimental units. The model for this type of experimental situation is a random effects model with more than one error terms and therefore estimation of variance components are concerned. A projection method is used for the calculation of sums of squares due to random components. Squared distances of projections instead of using the usual reductions in sums of squares that show how to use projections to estimate the variance components associated with the random components in the assumed model. Expectations of quadratic forms are obtained by the Hartley's synthesis as a means of calculation.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.92-98
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• 1998

Nonlinear finite element analyses of one dimensional consolidation problem were performed using an anisotropic hardening constitutive model. For the analyses, the anisotropic hardening elasto-plastic constitutive model based on the generalized isotropic hardening(GIH) rule was implemented into a nonlinear finite element analysis program, PLASTIC. In order to preserve the accuracy of the finite element solution for nonlinear problems, an implicit stress integration algorithm was employed. A consistent tangent moduli could also ensure the quadratic convergence of Newton's method. As a result, the nonlinear solution was accurately calculated and was converged to be asymptotically quadratic. In a consolidation problem, the relationship between load and settlement and between settlement and time vertical was analyzed comparing with results using the Cam-clay type model and the final consolidation settlement and the duration of primary consolidation could be evaluated rigorously using the GIH constitutive model.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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• pp.582-587
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• 1997

This paper deals with the control problem under nonlinear boiler systems with noise, and input constraints. MCMBPC(Multivariable Constrained Model-Based Predictive Controller) proposed by Wilkinson et al.[10,11] is used and nominal model is modified in this paper in order to applied to nonlinear boiler systems with feed-forward terms. The solution of the cost function optimization constrained on input and/or output variables is achieved using quadratic programming, via singular value decomposition(SVD). The controller designed is shown to satisfy the constraints and to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• The Pure and Applied Mathematics
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• v.16 no.4
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• pp.427-441
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• 2009

The method of Lattice Dynamical System is used to establish a global model on an infinite chain of many local markets interacting each other through a diffusion of prices between them. This global model extends the Walrasian evolutionary cobweb model in an independent single local market to the global market evolution. We assume that each local market has linear decreasing demands and quadratic supplies with naive predictors, and investigate the stationary behaviors of global price dynamics and show that their dynamics are conjugate to those of $H{\acute{e}}non$ maps and hence can exhibit complicated behaviors such as period-doubling bifurcations, chaos, and homoclic orbits etc.

• Coupled systems mechanics
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• v.13 no.1
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• pp.73-93
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• 2024

In this work, we propose combining an advanced optimal control algorithm with a geometrically exact beam model. For simplicity, the 2D Reissner beam model is chosen to represent large displacements and rotations. The difficulty pertains to the nonlinear nature of beam kinematics affecting the tangent stiffness matrix, making it non-constant, which compromises direct use of optimal control methods for linear problems. Thus, we seek to accommodate a time varying control using linear-quadratic regulator (LQR) algorithm with the proposed geometrically nonlinear beam model. We provide a detailed theoretical formulation and its numerical implementation in a variational format form. Several illustrative numerical examples are provided to confirm an excellent performance of the proposed methodology.

• Journal of the Korean Society of Tobacco Science
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• v.21 no.2
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• pp.109-118
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• 1999

A model predicted describing the effect of cigarette making materials on the level of filter ventilation was developed and evaluated. The developed model was expressed in terms of a linear and quadratic relationship which was validated with experimental measurements for different porosity of plug wrap and tipping paper, unencapsulated pressure drop of filter plug and cigarette column and vent position. Forty-six experimental frequencies were determined as a result of using three levels with five factors Box-Behnken design and analyzed by the multiple regression analysis with backward stepwise in STATISTICA/PC under restricted conditions. The four factors, except filter pressure drop variable, were statistically significant at the level of 0.05 but most of all linear by linear interactions were comparatively lower significant. By the analysis of linear and quadratic regression coefficient, filter ventilation of the cigarette was affected by porosity of plugwrap (5.87, -4.25), porosity of tip paper (5.68, -1.00), vent position (-3.87, 3.08), tobacco column pressure drop (2.56, 0.66), and filter pressure drop (1.50, 0.58) in the decreasing order. It should be emphasized that the major conclusion of this study was not that any particular parameter was linear or quadratic on any limit scale, but that there were highly significant relationships among factors involving linear, quadratic and their interaction and perhaps even linearity between and within factors. While, there is also quite strong evidence that vent position from mouth end and cigarette making materials are reverse relationship on this experimental model. On the basis of the result, it can be concluded that the porosity of the plug wrap and tipping paper has a marked effect on degree of filter ventilation rate. The F-value of plug wrap and tipping paper porosity among five factors were 39.2 and 36.8 respectively with P-value of 0.000 indicating higher significant for both factors. According to the analysis of variance, the model fitted for filter ventilation was significant at 5% confidence level and the coefficient of determination ($R^2$=0.84) was the proportion to variability in the data well fitted for by the model.