• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.64-69
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• 1993

In this paper we present the performance bounds of the optimal FIR filter in continuous time systems with modeling uncertainty. The performance measure bounds are calculated from the estimation error covariance bounds of the optimal FIR filter and the suboptimal FIR filter. Performance error bounds range are expressed by the upper bounds on the estimation error covariance difference between the real and nominal values in case of the systems with noise uncertainty or model uncertainty. The performance bounds of the systems are derived on the assumption that the system uncertainty and the estimation error covariance are imperfectly known a priori. The estimation error bounds of the optimal FIR filter is compared with those of the Kalman filter via a numerical example applied to the estimation of the motion of an aircraft carrier at sea, which shows the former has better performances than the latter.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.352-355
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• 1995

In this paper, estimation error bounds of the optimal FIR (Finite Impulse Response) filter, which is proposed by Kwon et al.[1, 2], are presented in discrete-time systems with the model uncertainty. Performance bounds are here represented by the upper bounds on the difference of the estimation error covariances between the nominal and real values in case of the systems with the noise or model parameter uncertainty. The estimation error bounds of the discrete-time optimal FIR filter is compared with those of the Kalman filter via a numerical example applied to the simulation problem by Toda and Patel[3]. Simulation results show that the former has robuster performance than the latter.

• Journal of Institute of Control, Robotics and Systems
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• v.1 no.1
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• pp.20-24
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• 1995

In this paper we analyze the performance bounds of the optimal FIR filter in continuous time systems with modeling uncertainty. The performance bounds are presented by the estimation error convariance and they are here expressed by the upper bounds of the difference of the estimation error covariance between the real and nominal values in case of the system with model uncertainties whose upper bounds are imperfrctly known a priori. The performance bounds of the optimal FIR filter are compared with those of the Kalman filter via a numerical example applied to the estimation of the motion of an aircraft carrier at sea, which shows the former has better performances than the latter.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.10
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• pp.895-901
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• 2012

The stability robustness problem of linear discrete systems with time-varying unstructured uncertainty of delayed states with time-varying delay time is considered. The proposed conditions for stability can be used for finding allowable bounds of timevarying uncertainty and delay time, which are solved by using LMI (Linear Matrix Inequality) and GEVP (Generalized Eigenvalue Problem) known as powerful computational methods. Furthermore, the conditions can imply the several previous results on the uncertainty bounds of time-invariant delayed states. Numerical examples are given to show the effectiveness of the proposed algorithms.

• Coupled systems mechanics
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• v.6 no.4
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• pp.487-499
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• 2017

This paper investigates the approximate solution bounds of radon diffusion equation in soil pore matrix coupled with uncertainty. These problems have been modeled by few researchers by considering the parameters as crisp, which may not give the correct essence of the uncertainty. Here, the interval uncertainties are handled by parametric form and solution of the relevant uncertain diffusion equation is found by using Galerkin's Method. The shape functions are taken as the linear combination of orthogonal polynomials which are generated based on the parametric form of the interval uncertainty. Uncertain bounds are computed and results are compared in special cases viz. with the crisp solution.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1492-1495
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• 1996

In this paper, an approach to autopilot design based on the robust nonlinear dynamic inversion method is proposed. Both unknown parameters and uncertainty bounds are estimated and parameter estimates are used in the fast inversion. Furthermore, to get more robustness slow inversion is incorporated with MRAC(Model Reference Adaptive Control) and sliding mode control where the estimates of uncertainty bounds are used. The proposed method is applied to the pitch autopilot design of a missile system and excellent performance is shown via computer simulation.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.9
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• pp.77-83
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• 2003

In this paper, a robust controller is proposed to control a robot manipulator which is governed by highly nonlinear dynamic equations. The controller is computationally efficient since it does not require the dynamic model or parameter values of a robot manipulator. It, however, requires uncertainty bounds which are derived by using properties of revolute joint robot dynamics. The stability of the robot with the controller is proved by Lyapunov theory. The results of computer simulations show that the robot system is stable, and has excellent trajectory tracking performance.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.52-54
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• 1991

In this paper, the robust stability, and the quadratic performance of linear uncertain systems are studied. A quadratic Lyapunov function candidate with time-varying matrix is derived to provide robust stability bounds. Also upper bounds of a quadratic performance is given under the assumption that the uncertain system is stable. Both the robust stability bounds and the upper bounds of a quadratic performance are obtained as solutions of a class of modified Lyapunov equations.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.5
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• pp.1-9
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• 2003

To predict the powering performance of full scale ships from the towing tank tests, resistance, propeller open water and self-propulsion tests are conducted. Model tests inevitably include the experimental error defined as the sum of two types of uncertainties, bias and precision errors. The induced errors in each element of model test are propagated through various routes and correlated with one another. The correlation coefficients are very important in the uncertainty analysis. The coefficient gives a direction(increase or decrease) for a value of error in individual elements. If the coefficient is not used accurately, the error bounds of the individual elements are overestimated or underestimated. In this study, the new methodology is applied to the uncertainty analysis of HMRI's towing tank tests, thus error bounds of each element is suggested and verified by several repetitive experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.963-967
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• 2000

This paper deals with a sliding mode control with uncertainty adaptation for the robust stabilization of input-delay systems with unknown uncertainties. A sliding surface including a state predictor is employed to compensate for the effect of the input delay. The proposed method does not need a priori knowledge of upper bounds on the norm of uncertainties, but estimates those upper bounds by adaptation laws based on the sliding surface. Then, a robust control law with the uncertainty adaptation is derived to ensure the existence of the sliding mode. A numerical example is given to illustrate the design procedure.