• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.177-182
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• 2013

This paper proposes an input-constrained reference tracking control of a converter model. To this end, first it is shown that the bilinear converter model can be equivalently represented by a linear uncertain model belonging to a polytopic set. Then, an input-constrained tracking control scheme for the linear uncertain model is designed based on recently proposed tracking control scheme. The control scheme yields not only a stabilizing control gain but also a feasible and invariant set for the converter model. Finally, simulation results show that the state trajectory always stays in the feasible and invariant set and that the output tracks the given reference while satisfying the input constraint.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.160-163
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• 1996

This paper deals with MCMBPC(Multivariable Constrained Model Based Predictive Controller) for nonlinear boiler system with noise and disturbance. MCMBPC is designed by linear state space model obtained from some operating point of nonlinear boiler system and Kalman filter is used to estimate the state with noise and disturbance. The solution of optimization of the cost function constrained on input and/or output variables is achieved using quadratic programming, viz. singular value decomposition (SVD). The controller designed is shown to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.73-77
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• 1986

Optimal input design problem for linear regression model with constrained output variance has been considered. It is shown that the optimal input signal for the linear regression model can also be realized as an ARMA process. Monte-Carlo simulation results show that the optimal stochastic input leads to comparatively better estimation accuracy than white input signal.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.697-699
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• 2000

In this paper, an extension of model predictive controller for nonlinear process using Takagi-Sugeno(TS) fuzzy model is proposed Since the consequent parts of TS fuzzy model comprise linear equations of input and output variables. it is locally linear, and the Generalized Predictive Control(GPC) technique which has been developed to control Linear Time Invariant(LTI) plants, can be extended as a parallel distributed controller. Also fuzzy soft constraints are introduced to handle both equality and inequality constraints in a unified form. So the traditional constrained GPC can be transferred to a standard fuzzy optimization problem. The proposed method conciliates the advantages of the fuzzy modeling with the advantages of the constrained predictive control, and the degree of freedom is increased in specifying the desired process behavior.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.697-703
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• 2014

Deal-or-No-Deal game is a famous TV show program of NBC, USA, which is composed of 10 stages at most. At each stage from the first and the ninth, a banker suggests a deal price to participants. In this paper, we intend to reveal the banker's deal price model using a constrained linear model and quadratic program. As results, we provide a linear model in relation to the deal price at each stage and then show using simulation data that the deal price is equal to the nearest integer of the value to be obtained by the provided linear model.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.511-520
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• 1999

An anti-reset windup (ARW) based compensation method for state constrained control systems is studied. First, a linear controller is constructed to give a desirable nominal performance ignoring state-constraints of a plant. Then, an additional compensator is introduced to provide smooth performance degradation under state-constraints of the plant. This paper focuses on the effective design method of the additional compensator. By minimizing a reasonable performance index, the proposed compensator is expressed in terms of theplant and ocntroller parameters. The resulting dynamics of the compensated controller exhibits the dominant part of the linear closed-loop system which can be seen from the singular perturbation model reducton theory. THe proposed method guarantees total stability of overall resulting systems if linear controllers were constructed to meet certain condition.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.27.4-27
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• 2001

A stabilizing control method for linear systems with model uncertainties and hard input constraints is developed, which does not require on-line optimizations. This work is motivated by the constrained robust MPC(CRMPC) approach [3] which adopts the dual mode prediction strategy (i.e. free control moves and invariant set) and minimizes a worst case performance criterion. Based on the observation that, a feasible control sequence for a particular state can be found as a linear combination of feasible sequences for other states, we suggest a stabilizing control algorithm providing sub-optimal and feasible control sequences using pre-computed optimal sequences for some canonical states. The on-line computation of the proposed method reduces to simple matrix multiplication.

• Journal of Korea Water Resources Association
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• v.37 no.5
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• pp.353-361
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• 2004

Effective storage in agricultural reservoir has been determined through the reservoir simulation operation based on the water budget analysis. Since each watershed has the native property for runoff, considering the runoff yielding from the basin is feasible to the determination of reservoir effective storage. In this study the stochastic linear programming model considering mainly runoff from watershed has been also formulated to analyze the effective storage of the exiting reservoir. The linear decision rule coupled with chance-constrained model in the linear programming model contributes to reduce the size of linear program model without considering the period of analysis. The Geum-Gang reservoir located in Ansung have been adopted to evaluate the effective storage. It has been shown that the effective storage based on the linear programming model is greater than that based on the water budget analysis. It has been also desired that once the effective storage is obtained through the linear programming model, operation of the reservoir should be performed to check the designed capacity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.1019-1023
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• 2012

This paper considers a model predictive control problem of discrete-time constrained systems with time-varying delay. For this problem, a delay dependent state feedback control approach is used to achieve asymptotic stabilization of systems with input constraints. Based on Lyapunov stability theory, a new stability condition is obtained via linear matrix inequality formulation to find cost monotonicity condition of the model predictive control algorithm which guarantee the closed loop stability. Finally, the proposed method is applied to a numerical example in order to show the effectiveness of our results.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.359-359
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• 2000

Generally, level control in the steam generator of a nuclear power plant is difficulty process control, because the low power operating can lead nonminimum phase characteristics(swell and shrink phenomenon) and flow measurement are unreliable and nonlinear characteristics. This paper presents a framework for solving this problem based on the constrained linear model predictive control and introduces the design of method for the level of the controller in the entire operating power of the steam generator, and compares with conventional PI controller.