• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.3
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• pp.407-414
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• 1996

In this paper we consider the variable structure control for a class of discrete-time uncertain multivariable systems where the nominal system is linear. Discrete-time switching dynamics are introduced so that a new type of state trajectories called sliding mode may exist on the sliding surface by state feedback. The quantitative analysis for the matched uncertainties will show that every response of the system with the proposed switching dynamics is bounded within small neighborhoods of the state-space origin. Also, by the similarity transformation it will be shown that the eigenvalues of the closed-loop systems are composed of those of the subsystems which govern the range-space dynamics and null-space dynamics. It will be also shown that ideal sliding mode can be obtained in the absence of uncertainties due to one-step attraction to the sliding surface regardless of initial position of states. (author). 12 refs., 2 figs.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.4
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• pp.303-310
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• 2020

Estimating the accurate internal state of lithium ion batteries to increase their safety and efficiency is crucial. Various algorithms are used to estimate the internal state of a lithium ion battery, such as the extended Kalman filter and sliding mode observer. A state-space model is essential in using algorithms to estimate the internal state of a battery. Two principal methods are used to express the state-space model, namely, continuous time and discrete time. In this work, the extended Kalman filter is employed to estimate the internal state of a battery. Moreover, this work presents and analyzes the estimation performance of algorithms consisting of a continuous time state-space model and a discrete time state-space model through static and dynamic profiles.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.680-684
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• 2005

This paper presents a data-based stability analysis of a MIMO linear time-invariant discrete-time system, as an extension of the previous results for a SISO system. In the MIMO case, a similar discussion as in the case of a SISO system is also applied, except that an augmented input and output space is considered whose dimension is determined in relation to both the orders of the input and output vectors and the numbers of inputs and outputs. As certain subspaces of the input and output space, both output data space and closed-loop data space are defined, which contain all the behaviors of a system, respectively, with zero input in open-loop and with a control input in closed-loop. Then, we can derive the data-based stability conditions, in which the open-loop stability can be checked by using a data matrix whose column vectors span the output data space and the closed-loop stability can also be checked by using a data matrix whose column vectors span the closed-loop data space.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.428-433
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• 1993

First, we propose a transparent and efficient design of discrete-time integral controllers accounting sampling skew. Based on the proposed controller, we derive a state-space representation of doubly coprime factorization including integral action. The representation is then used to obtain a convenient state-space parametrization of discrete-time two-degree-of-freedom integral controllers acconting sampling skew.

• 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.

• Proceedings of the KIEE Conference
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• 1986.07a
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• pp.140-144
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• 1986

In this paper an FIR(finite impulse response) filter and smoother are introduced for discrete time-invariant state-space models with driving noises. The FIR structure not only quarantees the BIBO stability and the robustness to parameter changes but also improves the filter divergence problem. It is shown that the impulse responses of the FIR filter and the smoother are obtained by Riccati-type difference equations and that they are to be time-invariant and reduced to very simple forms. For implementational purpose, recursive forms of the FIR filler and smoother are derived with each other used as the adjoint variable.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.244-249
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• 1998

In this paper, the hybrid state space self-tuning control technique Is studied within the framework of fuzzy systems and dual-rate sampling control theory. We show that fuzzy modeling techniques can be used to formulate chaotic dynamical systems. Then, we develop the hybrid state space self-tuning fuzzy control techniques with dual-rate sampling for digital control of chaotic systems. An equivalent fast-rate discrete-time state-space model of the continuous-time system is constructed by using fuzzy inference systems. To obtain the continuous-time optimal state feedback gains, the constructed discrete-time fuzzy system is converted into a continuous-time system. The developed optimal continuous-time control law is then convened into an equivalent slow-rate digital control law using the proposed digital redesign method. The proposed technique enables us to systematically and effective]y carry out framework for modeling and control of chaotic systems. The proposed method has been successfully applied for controlling the chaotic trajectories of Chua's circuit.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.150-155
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• 1997

Modelling and Simulation of the activation process for the myocardium is meaningful to understand special excitation conduction system in the heart and to study cardiac functions. In this paper, we propose two dimensional cellular automata model for the activation process of the myocardium and simulated by means of discrete time and discrete event algorithm. In the model, cells are classified into anatomically similar characteristic parts of heart; SA node, internodal tracks, AV node, His bundle, bundle branch and four layers of the ventricular muscle, each of which has a set of cells with preassigned properties, that is, activation time, refractory duration and conduction time between neighbor cell. Each cell in this model has state variables to represent the state of the cell and has some simple state transition rules to change values of state variables executed by state transition function. Simulation results are as follows. First, simulation of the normal and abnormal activation process for the myocardium has been done with discrete time and discrete event formalism. Next, we show that the simulation results of discrete time and discrete event cell space model is the same. Finally, we compare the simulation time of discrete event myocardium model with discrete time myocardium models and show that the discrete event myocardium model spends much less simulation time than discrete time myocardium model and conclude the discrete event simulation method Is excellent in the simulation time aspect if the interval deviation of event time is large.

• Journal of the Korea Society for Simulation
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• v.22 no.2
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• pp.11-19
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• 2013

This paper presents the battle space model, which is capable of propagating various types of emissions from platforms in underwater warfare simulation, predicting interesting encounters between pairs of platforms, and managing environmental information. The battle space model has four components: the logger, spatial encounter predictor (SEP), propagator, and geographic information system (GIS) models. The logger model stores brief data on all the platforms in the simulation, and the GIS model stores and updates environmental factors such as temperature and current speed. The SEP model infers an encounter among the platforms in the simulation, and progresses the simulation to the time when this encounter will happen. The propagator model receives various emissions from platforms and propagates these to other "within-range" platforms by considering the propagation losses and delays. The battle space model is based on the discrete event system specification (DEVS) and the discrete time system specification (DTSS) formalisms. To verify the battle space model, simple underwater warfare between a battleship and a submarine was simulated. The simulation results with the model were the same as the simulation results without the model.

• Korean Journal of Mathematics
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• v.21 no.2
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• pp.125-150
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• 2013

The stabilized Gauge-Uzawa method (SGUM), which is a second order projection type algorithm to solve the time-dependent Navier-Stokes equations, has been newly constructed in 2013 Pyo's paper. The accuracy of SGUM has been proved only for time discrete scheme in the same paper, but it is crucial to study for fully discrete scheme, because the numerical errors depend on discretizations for both space and time, and because discrete spaces between velocity and pressure can not be chosen arbitrary. In this paper, we find out properties of the fully discrete SGUM and estimate its errors and stability to solve the evolution Navier-Stokes equations. The main difficulty in this estimation arises from losing some cancellation laws due to failing divergence free condition of the discrete velocity function. This result will be extended to Boussinesq equations in the continuous research (part II) and is essential in the study of part II.