• 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 Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.21-25
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• 1996

In this paper we suggest a general purpose RNN training algorithm which is derived on the optimal control concepts and variational methods. First, learning is regared as an optimal control problem, then using the variational methods we obtain optimal weights which are given by a two-point boundary-value problem. Finally, the modified gradient descent algorithm is applied to RNN for on-line training. This algorithm is intended to be used on learning complex dynamic mappings between time varing I/O data. It is useful for nonlinear control, identification, and signal processing application of RNN because its storage requirement is not high and on-line learning is possible. Simulation results for a nonlinear plant identification are illustrated.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.2
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• pp.140-146
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• 2005

The on-line optimal control algorithm for central heating system has been researched for minimizing energy consumption while maintaining the comfort of indoor thermal environment in terms of the environmental variables such as indoor heating load and outdoor temperature variation. This study has been done by using TRNSYS Program in order to analyze the central heating system. The optimal control algorithm shows good energy Performances in comparison with the conventional one.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.12
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• pp.2131-2134
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• 2010

In this paper, the on-line system schemes for coordinated control system of multiple FACTS were presented to enhance the voltage stability around the metropolitan areas. In order to coordinated control system of FACTS devices, MFC on-line system calculates the optimal set point(Vref, Qrev) of FACTS devices using the coordinated control algorithm with real time network data which is transferred from SCADA/EMS system. If the system is unstable after contingencies, the new operation set-point of FACTS would be determined using bus sensitivity from tangent vector at voltage instability point. Otherwise, we would determine the new operation set-point of FACTS for considering economical operation, like as active power loss minimization using Optimal Power Flow algorithm. As the test, MFC(Multiple FACTS Coordinated control) on-line system will be installed in Korea power system.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.295-298
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• 1996

In this paper, a self-tuning optimal control algorithm is proposed to retain the optimal performance of an active suspension system, when the vehicle has some time varying parameters and parameter uncertainties. We consider a 2 DOF time-varying quarter car model which has the parameter variation of sprung mass, suspension spring constant and suspension damping constant. Instead of solving algebraic riccati equation on line, we propose a neural network approach as an alternative. The optimal feedback gains obtained from the off line computation, according to parameter variations, are used as the neural network training data. When the active suspension system is on, the parameters are identified by the recursive least square method and the trained neural network controller designer finds the proper optimal feedback gains. The simulation results are represented and discussed.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1012-1015
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• 1996

This study focuses on the optimal operation and control strategy of the fuel cell process. The control objective of the Phosphoric Acid Fuel Cell (PAFC) is established and dynamic modeling equations of the entire fuel cell process are formulated as discrete-time type. On-line optimal control of the MIMO system employs the direct decomposition-coordination method. The objective function is modified as the tracking form to enhance the response capability to the load change. The weight factor matrices Q,R, which are design parameters, are readjusted. This control system is compared with LQI method and the results show that the suggested method is better than the traditional method in pressure difference control.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.536-536
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• 2000

In this paper, we show based on Lyapunov theorem that the closed loop system with the constrained H$_{\infty}$ optimal controller is exponentially stable. Then the on-line feedback implementation of the constrained H$_{\infty}$ optimal control based on quadratic programs is proposed.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.3
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• pp.118-126
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• 2002

AVR parameter tuning for voltage control of generators has generally been done with the off-line open-circuit model of the synchronous generator. When the generator is connected on-line and operating with load the AVR operates in an entirely different environment from the open-circuit conditions. This paper describes a new method for AVR parameter tuning for on line conditions using SQP(Sequential Quadratic Programming) meshed with frequency response characteristics of linearized on-line system model. As the proposed method uses the un - line system model the tuned parameter sets show more optimal behavior in the on-line operating conditions. furthermore, as this method considers the performance indices that are needed for stable operation as constraints, AVR parameter sets that are tuned by this method could guarantee the stable performance, too.

• 전기의세계
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• v.49 no.9
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• pp.4-8
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• 2000

Recently we have shown based on Lyapunov theorem that the closed loop system with the constrained infinite horizon H$\infty$ optimal controller is exponentially stable. moreover the on-line feedback implementation of the constrained infinite horizon H$\infty$ optimal control based on quadratic programs has been proposed. n this paper we summarize and discuss these results.

• Journal of Advanced Marine Engineering and Technology
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• v.26 no.3
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• pp.299-306
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• 2002

The load frequency control(LFC) of power system is one of important subjects in view of system operation and control. That is, even though the rapid load disturbances are applied to the given power system, the stable and reliable power should be supplied to the users, converging unconditionally and rapidly the frequency deviations and the tie-line power flow ones of each area into allowable boundary limits. Nonetheless of such needs, if the internal parameter perturbation and the sudden load variation are given, the unstable phenomena of power systems can be often brought out because of the large frequency deviation and the unsuppressible power line one. So, an optimal modulation controller for UC of multi-area power system is designed by a recursive algorithm that determines the state weighting matrix Q of a linear quadratic performance criterion. The optimal modulation controller is based on optimal control and can obtain the exact dynamic response of the UC of multi-area power system in the time domain. The performances of the resultant optimal modulation control, that is, the steady-state deviations of frequency and tie-line power flow and the related dynamics, were investigated and analyzed in detail by being applied to the UC of multi-area power system in the perturbations of predetermined internal parameters. Through the simulation results tried variously in this paper for disturbance of stepwise load changes, the superiorities of the proposed optimal modulation controller in robustness and stability were proved.