• Bulletin of the Korean Mathematical Society
• /
• v.50 no.1
• /
• pp.263-273
• /
• 2013

A mean-value result, saying that the difference quotient of a differentiable function in a real interval is a mean value of its derivatives at the endpoints of the interval, leads to the functional equation $$\frac{f(x)-F(y)}{x-y}=M(g(x),\;G(y)),\;x{\neq}y$$, where M is a given mean and $f$, F, $g$, G are the unknown functions. Solving this equation for the arithmetic, geometric and harmonic means, we obtain, respectively, characterizations of square polynomials, homographic and square-root functions. A new criterion of the monotonicity of a real function is presented.

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

• The Pure and Applied Mathematics
• /
• v.13 no.4 s.34
• /
• pp.295-301
• /
• 2006

We Investigate the relation between the multi-variable bi-additive functional equation f(x+y+z,u+v+w)=f(x,u)+f(x,v)+f(x,w)+f(y,u)+f(y,v)+f(y,w)+f(z,u)+f(z,v)+f(z,w) and the multi-variable quadratic functional equation g(x+y+z)+g(x-y+z)+g(x+y-z)+g(-x+y+z)=4g(x)+4g(y)+4g(z). Furthermore, we find out the general solution of the above two functional equations.

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

• Proceedings of the Korea Concrete Institute Conference
• /
• 2003.11a
• /
• pp.643-646
• /
• 2003

This study concerns the new estimated strength equation of concrete by penetration test. There are not only few estimate strength equations of concrete, but also many problems to apply them because of time, cost, easiness, structural damage, reliability and so on. In this study, there performed a series of test for one year and estimated strength equation of concrete as follows; Linear: fck =3.38d - 95.1 ($$r^2$$=88.6%) Quadratic: fck =0.188$$d^2$$- 10.76d + 166.3 ($$r^2$$=96.7%) here, fck : estimated compressive strength of concrete by Mpa d: exposed probe length by mm.

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

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.2
• /
• pp.131-138
• /
• 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. In this paper, an alternative time-invariant model is proposed, the design method and the stability of the LQG (Linear Quadratic Gaussian) control scheme for the realization are presented. The realization is flexible to construct to the sampling rate variations, the closed-loop system is shown to be asymptotically stable even in the inter-sampling intervals and it has smaller computation in on-line control loop than the previous time-invariant realizations.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.8 s.173
• /
• pp.34-41
• /
• 2005

Generally, it is not easy to get a suitable controller for multi variable systems. If the modeling equation of the system can be found, it is possible to get LQR control as an optimal solution. This paper suggests an LQR learning method to design LQR controller without the modeling equation. The proposed algorithm uses the same cost function with error and input energy as LQR is used, and the LQR controller is trained to reduce the function. In this training process, the Jacobian matrix that informs the converging direction of the controller Is used. Jacobian means the relationship of output variations for input variations and can be approximately found by the simple experiments. In the simulations of a hydrofoil catamaran with multi variables, it can be confirmed that the training of LQR controller is possible by using the approximate Jacobian matrix instead of the modeling equation and this controller is not worse than the traditional LQR controller.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.5
• /
• pp.561-566
• /
• 1998

This paper presents an algebraic approach to optimal control for time invariant continuous system using STWS(single term Walsh series). In optimal control, it is well known that the design problem with quadratic performance criteria often involves the determination of time-varying feedback gain matrix by solving the matrix nonlinear Riccati equation and of command signal by solving the integral equation, which makes design procedure quite difficult. Therefore, in order to resolve this problem, this paper is introduced to STWS. In this paper, the time-varying feedback gains and command signals are determined by piecewise constant gains which can be easily obtained from algebraic equation using STWS.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.32A no.8
• /
• pp.140-152
• /
• 1995

Derived was the scalar wave equation of optical fibers. Based on the derived equation, the dispersion characteristics of arbitrarily profiled fibers were analyzed. We applied the 1-D FEM employing quadratic interpolation fucntions to solve the scalar wave equation. To find the optimum index distribution of a fiber that has the ultra-low total dispersion, we analyzed QC fibers as objects. Adding 2$_{nd}$ and 3$_{rd}$ clads to DC fiber, we investigated the change of dispersion characteristics. We found the QC fiber parameters for which the dispersion was ultra-low flattened, less than 0.5 ps/km.nm for ${\lambda}=1.4~1.6{\mu}m$, and the dispersion value was as low as 0.20 ps/km.nm at ${\lambda}=1.55{\mu}m$.