• 제어로봇시스템학회:학술대회논문집
• /
• 1988.10b
• /
• pp.1051-1054
• /
• 1988

In this paper we present initial convergence properties of the Kalman filtering algorithm, we put an arbitrary small positive correlation matrix as an initial condition in the recursive algorithm. This arbitrary small initial condition perturbs the Kalman filtering algorithm and may lead to initial instability. We derive a condition which insures the stable operation of the Kalman filtering algorithm from the stochastic Lyapunov difference equation.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.3
• /
• pp.263-272
• /
• 2000

In this paper we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastcic approximation method it is regarded as a stochastic recursive filter algorithm. In addition we provide a filtering and control condition for a stochastic nonlinear system called the perfect filtering condition in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable and the proposed neural controller is more efficient than the conventional LQG controller and the canonical LQ-Neural controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.346-352
• /
• 1998

In this paper, we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastic approximation method, it is regarded as a stochastic recursive filter algorithm. In addition, we provide a filtering and control condition for a stochastic nonlinear system, called perfect filtering condition, in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence, the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable. and the proposed neural controller is more efficient than the conventional LQG controller and the canoni al LQ-Neural controller.

• Proceedings of the KIEE Conference
• /
• 2001.11c
• /
• pp.18-21
• /
• 2001

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2008.03a
• /
• pp.419-426
• /
• 2008

Recently, localized heavy rain due to "EL-LIO" was a kind of reason by risk of slope stability. In this paper, the behaviour of slope when localized heavy rain was studied. In order to perform this study experimental programs were performed. Experimental programs was checked filtering conditions for slope stability due to localized heavy rain. And then, investigated slope stability and fracture mechanism each other types. In the experimental study, performed changing filtering condition by embankment, through five fixing factors such as rainfall intensity, slope shape, geological condition, compaction energy and water content. According to the results of this study, behaviour of facture slope has made a shallow and narrow waterway. This waterway expanded base stone. In order to, suggested a system for slope stability examination.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.4
• /
• pp.502-508
• /
• 2014

In this paper, a delay-dependent $H_{\infty}$ filtering problem is investigated for discrete-time delayed nonlinear systems which include a more general sector nonlinear function instead of employing the commonly used Lipschitz-type function. By using the Lyapunov-Krasovskii functional approach, a less conservative sufficient condition is established for the existence of the desired filter, and then, the corresponding solvability condition guarantee the stability of the filter with a prescribed $H_{\infty}$ performance level. Finally, two simulation examples are given to show the effectiveness of the proposed filtering scheme.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.18-18
• /
• 2000

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network are provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shows that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.33 no.6
• /
• pp.475-482
• /
• 2020

For electronic paper displays using electrophoresis, the response time and reflectivity of the image panel fabricated by filtering are analyzed. For the filtering process, a square wave and ramp wave are applied to white charged particles with a unique q/m value. We divide the sample panels into #1 to #4 according to the applied waveform in the filtering process. Step waves comprising two steps are used to drive the panel; therefore, we divide the driving conditions into D1~D4. The applied voltage at the first stage of the half cycle of the driving waveform moves the charged particles attached via the image force from the electrode, and the applied voltage at the second stage moves the floating charged particles by detaching. As mentioned, four types of driving conditions (D1 to D4) classified according to the half cycle of the driving waveform are applied to the samples #1 to #4), which are classified according to four types of filtering process. When driving condition D1 is applied to the four types of sample panels, the rise time of #1 is 1.59s, #2 is 1.706s, #3 is 1.853s, and #4 is 1.235s, resulting in #4 being relatively faster compared with other sample panels, and showing the same trend in other driving conditions. As a result, we confirm that applying the driving condition D1 causes abrupt movement of the white charged particles injected into the cell. When the same driving waveform (D1) is applied to each sample, reflectivities of 32.1% for #1, 31.4% for #2, 27.9% for #3, and 63.4% for #4 are measured. From the experiment, we confirm that the driving condition D1 (1s of 3.5 V, 9s of 3.0 V) and ramp wave #4 in filtering are desirable for good reflectivity and response time. Our research is expected to contribute to the improvement of the filtering process and optimization of the driving waveform.

• International Journal of Control, Automation, and Systems
• /
• v.4 no.6
• /
• pp.697-704
• /
• 2006

The paper is concerned with $H_{\infty}$ filtering for descriptor systems. A necessary and sufficient condition is established in terms of linear matrix inequalities(LMIs) for the existence of normal filters such that the error systems are admissible and the transfer function from the disturbance to the filtering error output satisfies a prescribed $H_{\infty}$-norm bound constraint. When these LMIs are feasible, an explicit parameterization expression of all desired normal filter is given. All these results are yielded without decomposing the original descriptor systems, which makes the filter design procedure simple and direct. Finally, a numerical example is derived to demonstrate the applicability of the proposed approach.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.7 no.4
• /
• pp.221-227
• /
• 2007

This paper introduces an on-line particle-filtering-based framework for failure prognosis in nonlinear, non-Gaussian systems. This framework uses a nonlinear state-space model of the plant(with unknown time-varying parameters) and a particle filtering(PF) algorithm to estimate the probability density function(pdf) of the state in real-time. The state pdf estimate is then used to predict the evolution in time of the fault indicator, obtaining as a result the pdf of the remaining useful life(RUL) for the faulty subsystem. This approach provides information about the precision and accuracy of long-term predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary carrier plate are used to validate the proposed methodology.