• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.608-616
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• 2018

If a general nonlinear system is linearized by the successive multiplication of the 1st and 2nd order systems, then there are four types of poles in this linearized system: the pole of the 1st order system and the equal poles, two distinct real poles, and complex conjugate pair of poles of the 2nd order system. Linear Quadratic (LQ) control is a method of designing a control law that minimizes the quadratic performance index. It has the advantage of ensuring the stability of the system and the pole placement of the root of the system by weighted matrix adjustment. LQ control by the weighted matrix can move the position of the pole of the system arbitrarily, but it is difficult to set the weighting matrix by the trial and error method. This problem can be solved using the characteristic equations of the Hamiltonian system, and if the control weighting matrix is a symmetric matrix of constants, it is possible to move several poles of the system to the desired closed loop poles by applying the control law repeatedly. The paper presents a method of calculating the state weighting matrix and the control law for moving the equal poles with Jordan blocks to two real poles using the characteristic equation of the Hamiltonian system. We express this characteristic equation with a state weighting matrix by means of a trigonometric function, and we derive the relation function (${\rho},\;{\theta}$) between the equal poles and the state weighting matrix under the condition that the two real poles are the roots of the characteristic equation. Then, we obtain the moving-range of the two real poles under the condition that the state weighting matrix becomes a positive semi-finite matrix. We calculate the state weighting matrix and the control law by substituting the two real roots selected in the moving-range into the relational function. As an example, we apply the proposed method to a simple example 3rd order system.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.7 no.4
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• pp.566-576
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• 1995

A Computational code has been developed in order to design axial fans by the numerical optimization techniques incorporated with flow analysis code solving three-dimensional Navier-Stokes equation. The steepest descent method and the conjugate gradient method are used to look for the search direction in the design space, and the golden section method is used for one-dimensional search. To solve the constrained optimization problem, sequential unconstrained minimization technique, SUMT, is used with imposed quadratic extended interior penalty functions. In the optimization of two-dimensional cascade design, the ratio of drag coefficient to lift coefficient is minimized by the design variables such as maximum thickness, maximum ordinate of camber and chord wise position of maximum ordinate. In the application of this numerical optimization technique to the design of an axial fan, the efficiency is maximized by the design variables related to the sweep angle distributed by quadratic function along the hub to tip of fan.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2946-2948
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• 2000

In this paper, we proposed the Fuzzy Polynomial Neural Networks(FPNN) model with fuzzy activation node. The proposed FPNN structure is generated from the mutual combination of PNN(Polynomial Neural Networks) structure and fuzzy inference system. The premise of fuzzy inference rules defines by triangular and gaussian type membership function. The fuzzy inference method uses simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used. The structure of FPNN is not fixed like in conventional Neural Networks and can be generated. The design procedure to obtain an optimal model structure utilizing FPNN algorithm is shown in each stage. Gas furnace time series data used to evaluate the performance of our proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.95-99
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• 1993

In this paper, we discuss H$_{\infty}$ robust performance problem for uncertain system described in a descriptor form. We show that the method based on Riccati equation can be extended to solve this problem. First, such a sufficient condition is given that the system described in a descriptor form is quadratic stable and H$_{\infty}$ norm of a specified transfer function is less than a given level. Using this result, a state feedback law which ensures H$_{\infty}$ robust performance of closed loop system is derived based on a positive definite solution of a Riccati equation. This result shows that a solution of the problem can be also obtained by solving H$_{\infty}$ standard problem for an extended plant. Finally, a design example and simulation results will be given.ven.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.81-84
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• 1996

We propose the robust nonlinear controller design methodology, the $H_{\infty}$ constrained quasi - linear quadratic Gaussian control (QLQG/ $H_{\infty}$), for the statistically-linearized multivariable system with hard nonlinearties such as Coulomb friction, deadzone, etc. The $H_{\infty}$ performance constraint is involved in the optimization process by replacing the covariance Lyapunov equation with the Riccati equation whose solution leads to an upper bound of the QLQG performance. Because of the system's nonlinearity, however, one equation among three Riccati equations contain the nonlinear correction terms that are very difficult to solve numerically. To treat this problem, we use simple algebraic techniques. With some analytic transformation for Riccati equations, the nonlinear correction terms can be so eliminated that the set of a linear controller to the different operating points are designed. Synthesizing these via inverse random input describing function (IRIDF) technique, the final nonlinear controller can be designed.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.123-130
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• 1999

In discrete-time controlled system, sampling time is one of the critical parameters for control performance. It is useful to employ different sampling rates into the system considering the feasibility of measuring system or actuating system. The systems with the different sampling rates in their input and output channels are named multirate system. Even though the original continuous-time system is time-invariant, it is realized as time-varying state equation depending on multirate sampling mechanism. By means of the augmentation of the inputs and the outputs over one period, the time-varying system equation can be constructed into the time-invariant equation. The two multirate formulations have some trade-offs in the simplicity to construct the controller, the control performance. It is good issue to determine the suitable formulation in consideration of performance of them. In this paper, the two categories of multirate formulations will be compared in terms of the linear quadratic (LQ) cost function. The results are used to select the multirate formulation and the sampling rates suitable to the desired control performance.

• Transactions of Materials Processing
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• v.8 no.3
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• pp.252-261
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• 1999

The 3-dimensional finite element program is developed to analyze the non-isothermal forming processes of aluminum-alloy sheet metals. Bishop's method is introduced to solve the heat balance and force equilibrium equations. Also, Barlat's non-quadratic anisotropic yield function depicts the planar anisotropy of the aluminum-alloy sheet. To find an appropriate constitutive equation, four different forms are reviewed. For the verification of the reliability of the developed program, the computational try-outs of the non-isothermal cylindrical cupping processes of AL5052-H32 and Al1050-H16 are carried out. As results, the constitutive equation relating to strain and strain-rate, in which the constants are represented by the 5th-degree polynomials of temperature, is in good agreement with measurement. The computational try-outs can predict optimal forming conditions in non-isothermal forming processes.

• Journal of the Korean Mathematical Society
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• v.39 no.2
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• pp.319-330
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• 2002

The method of quasilinearization generates a monotone iteration scheme whose iterates converge quadratically to a unique solution of the problem at hand. In this paper, we apply the method to two families of three-point boundary value problems for second order ordinary differential equations: Linear boundary conditions and nonlinear boundary conditions are addressed independently. For linear boundary conditions, an appropriate Green\`s function is constructed. Fer nonlinear boundary conditions, we show that these nonlinearities can be addressed similarly to the nonlinearities in the differential equation.

• The Mathematical Education
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• v.55 no.3
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• pp.335-355
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• 2016

The purpose of this study is to analyze tasks to find intersection points of a function and its inverse function. To do this, we produced a task and 64 people solved the task. As a result, most people had a cognitive conflict related to inverse function. Because of over-generalization, most people regarded intersection points of a function and y=x as intersection points of a function and its inverse. To find why they used the method to find intersection points, we investigated 10 mathematics textbooks. As a result, 23 tasks were related a linear function, quadratic function, or irrational function. 21 tasks were solved by using an equation f(x)=x. 3 textbooks presented that a set of intersection points of a function and its inverse was not equal to a set of intersection points of a function and y=x. And there was no textbook to present that a set of intersection points of a function and its inverse was equal to a set of intersection points of $y=(f{\circ}f)(x)$ and y=x.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.5
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• pp.173-178
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• 1985

The control of a linear system with random coefficients is discussed here. The cost function is of a quadratic form and the random coefficients are assumed to be completely observable by the controller. Stochastic Process involved in the problem by the controller. Stochastic Process involved in the problem formulation is presented to be the unique strong solution to the corresponding stochastic differential equations. Condition for the optimal control is represented through the existence of solution to a Cauchy problem for the given nonlinear partial differential equation. The optimal control is shown to be a linear function of the states and a nonlinear function of random parameters.