• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.524-529
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• 2004

In this paper, We described the method which developed the optimal control system for air separation unit to change production rates frequently and rapidly. Control models of the process were developed from actual plant data using subspace identification method which is developed by many researchers in resent years. The model consist of a series connection of linear dynamic block and static nonlinear block (Wiener model). The model is controlled by model based predictive controller. In MPC the input is calculated by on-line optimization of a performance index based on predictions by the model, subject to possible constraints. To calculate the optimal the performance index, conditions are expressed by LMI(Linear Matrix Inequalities).In order to access at the Bailey DCS system, we applied the OPC server and developed the Client program. The OPC sever is a device which can access Bailey DCS system.The Client program is developed based on the Matlab language for easy calculation,data simulation and data logging. Using this program, we can apply the optimal input to the DCS system at real time.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.708-710
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• 2004

This paper studies a multirate sampled-data control for LTI systems by using the digital redesign (DR) method. In this note, to well tackle the problem associated with both the state matching and the stabilization, our nobel strategy is to minimize the linear quadratic cost function. The main features of the proposed method are that i) the delta-operator-based descretization method is applied to improve the state-matching performance in the fast sampling limit and/or the large input multiplicity; ii) the proposed multirate control scheme can improve the state-matching performance in the long sampling limit; iii) some sufficient conditions that guarantee the stability of the closed-loop discrete-time system and provide a guarantee cost for the cost function can be formulated in the LMIs format; and iv) an optimal sampled-data controller in the sense of minimizing the upper bound of the cost function is also given by means of an LMI optimization procedure.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.529-534
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• 2005

In this paper, a new type of output feedback control, called a $H_2/H_{\infty}$ fnite memory control (FMC), is proposed for deterministic state space systems. Constraints such as linearity, unbiasedness property, and finite memory structure with respect to an input and an output are required in advance to design $H_2/H_{\infty}$ FMC in addition to the performance criteria in both $H_2$ and $H_{\infty}$ sense. It is shown that $H_2$, $H_{\infty}$, and mixed $H_2/H_{\infty}$ FMC design problems can be converted into convex programming problems written in terms of linear matrix inequalities (LMIs) with some linear equality constraints. Through simulation study, it is illustrated that the proposed $H_2/H_{\infty}$ FMC is more robust against uncertainties and faster in convergence than the existing $H_2/H_{\infty}$ output feedback control schemes.

• Journal of Mechanical Science and Technology
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• v.16 no.1
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• pp.75-82
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• 2002

A simultaneous optimal design problem of structural and control systems is discussed by taking a 3-D truss structure as an object. We use descriptor forms for a controlled object and a generalized plant because the structural parameters appear naturally in these forms. We consider a minimum weight design problem for structural system and disturbance suppression problem for the control system. The structural objective function is the structural weight and the control objective function is $H_{\infty}$ norm from the disturbance input to the controlled output in the closed-loop system. The design variables are cross sectional areas of the truss members. The conditions for the existence of controller are expressed in terms of linear matrix inequalities (LMI) By minimizing the linear sum of the normalized structural objective function and control objective function, it is possible to make optimal design by which the balance of the structural weight and the control performance is taken. We showed in this paper the validity of simultaneous optimal design of structural and control systems.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.297-298
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• 2007

Repetitive systems stand for a kind of systems that perform a simple task on a fixed pattern repetitively and are widely spread in industrial fields. Hence, those systems have been of much interests by many researchers, especially in the field of iterative learning control (ILC). In this paper, we propose a finite-horizon tracking control scheme for linear time-varying repetitive systems with uncertain initial conditions. The scheme is derived both analytically and numerically for state-feedback systems and only numerically for output-feedback systems. Then, it is extended to stable systems with input constraints. All numerical schemes are developed in the forms of linear matrix inequalities. A distinguished feature of the proposed scheme from the existing iterative learning control is that the scheme guarantees the tracking performance exactly even under uncertain initial conditions. The simulation results demonstrate the good performance of the proposed scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.7A
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• pp.967-977
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• 2000

In this paper, we apply a mixed $H_2/H_{\infty}$ control to a generalized plant of inverted pendulum system represented by an LFR(Linear Fractional Representation). First, in order to obtain the generalized plant, the linear model of the inverted pendulum represented by an LFR(Linear fractional Representation) is derived. In LFR, we consider system uncertainties as three nonlinear components and a pendulum mass uncertainty. Augmenting the LFR model by adding weighting functions, we get a generalized plant. And then, we design a mixed $H_2/H_{\infty}$ controller for the generalized plant. In order to design the mixed $H_2/H_{\infty}$ controller, we use the LMI technique. To evaluate control performances and robust stability of the mixed $H_2/H_{\infty}$ controller designed, we compare it with the $H_{\infty}$ controller through the simulation and experiment. In the result, with the fewer feedback information, the mixed $H_2/H_{\infty}$ controller shows the better control performances and robust stability than the $H_{\infty}$ controller in the sense of pendulum angle.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.6
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• pp.46-54
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• 1998

This paper presents a method for designing robust fuzzy $H^{\infty}$ controllers which stabilize nonlinear systems with parameter uncertainty adn guarantee an induced $L_{2}$ norm bound constraint on disturbance attenuation for all admissible uncertainties. Takagi and Sugeno's fuzzy models with uncertainty are used as the model for the uncertain nonlinear systems. Fuzzy control systems utilize the concept of so-called parallel distributed compensation(PDC). Using a single quadratic Lyapunov function, the stability condition satisfying decay rate and disturbance attenuation condition for Takagi and Sugeno's fuzzy model with parameter uncertainty are discussed. A sufficient condition for the existence of robust fuzzy $H^{\infty}$ controllers is then presented in terms of linear matrix inequalities(LMIs). Finally, design examples of robust fuzzy $H^{\infty}$ controllers for uncertain nonlinear systems are presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.337-342
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• 2009

This paper concerns delay-range-dependent robust stability and stabilization for time-delay nonliner system via T-S fuzzy model approach. The time delay is assumed to be a time-varying continuous function belonging to a given range. On the basis of a novel Lyapunov-Krasovskii functional, which includes the information of the range, delay-range-dependent stability criteria are established in terms of linear matrix inequality. It is shown that the new criteria can provide less conservative results than some existing ones. Moreover, the stability criteria are also used to design the stabilizing state-feedback controllers. Numerical examples are given to demonstrate the applicability of the proposed approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.239-245
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• 2002

In this Paper, we present a method for designing fuzzy $H_2/H_{\infty}$ controllers of delayed nonlinear systems with saturating input. Takagi-Sugeno fuzzy model is employed to represent delayed nonlinear systems with saturating input. The fuzzy control systems utilize the concept of the so-called parallel distributed compensation(PDC). Using a single quadratic Lyapunov function, the globally exponential stability and $H_2/H_{\infty}$ performance problem are discussed. And a sufficient condition for the existence of fuzzy $H_2/H_{\infty}$ controllers is given in terms of linear matrix inequalities(LMIs). The designing fuzzy $H_2/H_{\infty}$ controllers minimize an upper bound on a linear quadratic performance measure. Finally, a design example of fuzzy $H_2/H_{\infty}$ controller for uncertain delayed nonlinear systems with saturating input.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.285-290
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• 2007

In this paper, we presents robust digital redesign (DR) method for observer-based linear time-invariant (LTI) system. The term of DR involves converting an analog controller into an equivalent digital one by considering two condition: state-matching and stability. The design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between interpolated linear operators to be matched. Also, by using the bilinear and inverse bilinear approximation method, we analyzed the uncertain parts of given observer-based system more precisely, When a sampling period is sufficiently small, the conversion of a analog structured uncertain system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the state-matching of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs).