• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1014-1022
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• 2002

An objective of Automated Highway Systems (AHS) is to increase the safety and throughput of the existing highway infrastructure by introducing traffic automation. AHS is an example of a large scale, multiagent complex dynamical system and is ideally suited for a hierarchical hybrid controller. We discuss a design issue of efficient hybrid controllers for the platoon maneuvers on AHS. For the modeling of a hybrid system including the merge and split operations, a safety distance policy is introduced for the merge and split operations. After that, the platoon system will be modeled by a hybrid system In addition, a hybrid controller for the proposed merge and split operation models is presented. Finally, the performance of the proposed hybrid control scheme is demonstrated via scenarios for platoon maneuvers.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.336-339
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• 2001

The ozone forecasting systems have many problems because the mechanism of the ozone concentration is highly complex, nonlinear, and nonstationary. Especially, the performance of the prediction results in the high-level ozone concentration are not good. This paper describes the modeling method of the ozone prediction system using neuro-fuzzy approaches and fuzzy clustering methods. The dynamic polynomial neural network (DPNN) based upon a typical algorithm of GMDH (group method of data handling) is a useful method for data analysis, the identification of nonlinear complex systems, and prediction of dynamical systems.

• International Journal of Safety
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• v.11 no.1
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• pp.26-32
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• 2012

This paper introduces a feasibility evaluation method for prognosis systems based on an empirical model in nuclear power plants. By exploiting the dynamical signature characterized by abnormal phenomena, the prognosis technique can be applied to detect the plant abnormal states prior to an unexpected plant trip. Early $operator^{\circ}{\emptyset}s$ awareness can extend available time for operation action; therefore, unexpected plant trip and time-consuming maintenance can be reduced. For the practical application in nuclear power plant, it is important not only to enhance the advantages of prognosis systems, but also to quantify the negative impact in prognosis, e.g., uncertainty. In order to apply these prognosis systems to real nuclear power plants, it is necessary to conduct a feasibility evaluation; the evaluation consists of 4 steps (: the development of an evaluation method, the development of selection criteria for the abnormal state, acquisition and signal processing, and an evaluation experiment). In this paper, we introduce the feasibility evaluation method and propose further study points for applying prognosis systems from KHNP's experiences in testing some prognosis technologies available in the market.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.314-315
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• 1992

A method is developed for the detection of parametric change for conditionally-linear stochastic systems. Such systems are quite prevalent in biology, economics and engineering, and may be synthesized as certain bilinear stochastic systems with output feedback (possibly nonlinear) through the controls. In other words, these systems are linear in the "unmeasured states" and nonlinear in the "measured states." The conditionally linear filter developed by Kolodziej forms a convenient part of the algorithm which estimates the time of change of parameter values.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.439-445
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• 1990

All models of dynamical systems invariably have some measure of uncertainties associated with some of their dynamics. The recent approaches to establish robustness of stabilizing feedback control against the possible uncertainties have a serious limitation, that is their applicability only to the systems that satisfy the matching conditions. Such conditions are rarely met in general applications. If a particular system satisfies the matching conditions, the addition of an actuator will destroy the satisfaction of such conditions. In this paper, we develop robust control algorithm for uncertain multivariable systems in which the matching conditions are not necessarily met. We empoly Lyapunov's second method to derive robust stabilizing controllers which guarantee asymptotic stability against prescribed uncertainties. The derivation consists of transforming the original uncertain system to controllable canonical form and constructing a constant switching surface by designing the closed-loop characteristics as a function of the uncertainties. Numerical examples are discussed as illustrations.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.15-18
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• 2007

We consider the problem of designing a static output feedback sliding mode control law for linear dynamical systems with mismatched uncertainties in the state matrix. We assume that an output dependent sliding surface guaranteeing the quadratic stability of the sliding mode dynamics is given, the reachability condition is not required to be satisfied globally, and the output feedback sliding mode control law complises both linear and discontinuous parts. We reduce the problem of designing the linear part of the sliding mode control law to a simple LMI problem which offers design flexibility for combining various useful convex design specifications. Our approach does not require state transformation and it can be applied to mismatched uncertain systems.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.10-21
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• 2023

For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of noise are still required. In this paper, we propose the recurrent neural-network based Kalman filter, where its Kalman gain is obtained via the proposed GRU-LSTM based neural-network framework that does not need the precise model information as well as the noise covariance information. By the proposed neural-network based Kalman filter, the state estimation performance is enhanced in terms of the tracking error, which is verified through various linear and nonlinear tracking problems with incomplete model and statistical covariance information.

• Atmosphere
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• v.23 no.1
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• pp.73-83
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• 2013

In recent several years, East Asia, Europe and North America have suffered successive cold winters and a number of historical records on the extreme weathers are replaced with new record-breaking cold events. As a possible explanation, several studies suggested that cryospheric conditions of Northern Hemisphere (NH), i.e. Arctic sea-ice and snow cover over northern part of major continents, are changing significantly and now play an active role for modulating midlatitude atmospheric circulation patterns that could bring cold winters for some regions in midlatitude. In this study, a dynamical seasonal prediction system for NH winter is newly developed using the snow depth initialization technique and statistically predicted sea-ice boundary condition. Since the snow depth shows largest variability in October, entire period of October has been utilized as a training period for the land surface initialization and model land surface during the period is continuously forced by the observed daily atmospheric conditions and snow depths. A simple persistent anomaly decaying toward an averaged sea-ice condition has been used for the statistical prediction of sea-ice boundary conditions. The constructed dynamical prediction system has been tested for winter 2012/13 starting at November 1 using 16 different initial conditions and the results are discussed. Implications and a future direction for further development are also described.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.859-872
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• 2008

In this paper, the problem of partial asymptotic stabilization of nonlinear control cascaded systems with integrators is considered. Unfortunately, many controllable control systems present an anomaly, which is the non complete stabilization via continuous pure-state feedback. This is due to Brockett necessary condition. In order to cope with this difficulty we propose in this work the partial asymptotic stabilization. For a given motion of a dynamical system, say x(t,$x_0,t_0$)=(y(t,$y_0,t_0$),z(t,$z_0,t_0$)), the partial stabilization is the qualitative behavior of the y-component of the motion(i.e., the asymptotic stabilization of the motion with respect to y) and the z-component converges, relative to the initial vector x($t_0$)=$x_0$=($y_0,z_0$). In this work we present new results for the adding integrators for partial asymptotic stabilization. Two applications are given to illustrate our theoretical result. The first problem treated is the partial attitude control of the rigid spacecraft with two controls. The second problem treated is the partial orientation of the underactuated ship.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1993-1998
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• 2004

This paper proposes a new framework for control system design, called the data-based control approach or data space approach, in which the input and output data of a dynamical system is directly and solely used to analyze or design a control system without the employment of any mathematical models like transfer functions, state space equations, and kernel representations. Since, in this approach, most of the analysis and design processes are carried out in the domain of the data space, we introduce some notions of geometrical objects, e.g., the openloop and closed-loop data spaces, which serve as the system representations in the data space. In addition, we establish a relationship between the open-loop and closed-loop data spaces that the closed-loop data space is contained in the open-loop data space as one of its subspaces. By using this relationship, we can derive the data-based stabilization condition for a linear time-invariant discrete-time system, which leads to a linear matrix inequality with a rank constraint.