• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.670-672
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• 1995

This paper deals with the robust two-degree-of-freedom multivariable control system using $H_{2}/H{\infty}$optimization method which can achieve the robust stability and the robust performance, simultaneously. The feedback controller can obtain the robust stability property. The feedforward controller can obtain the robust performance property under modelling error. The robust two-degree-of-freedom multivariable control system is applied to the nonlinear multivariable boiler-turbine system. The validity of the proposed method is verified though being compared with LQG/LTR design method.

• 전기의세계
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• v.31 no.4
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• pp.296-302
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• 1982

The computer algorithm and program are developed to obtain the Luenberger Canonical form and the transform matrices for linear time invariant multivariable control systems. The model controller of an eigth order system, which assigns the modes of the multivariable control systems and closed-loop matrices are computed numerically by the developed programs. It is shown that the computed results coincide with the Luenberger's and Kalman's method. The gain of the model controller has varied from 10$^{-3}$ to 10$^{5}$ by the modes assignment of the open-loop system.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.456-458
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• 1986

A method for on-line tuning of the PID-controller parameters for a discrete-time multivariable process system is presented. And it is based on a step change in the controller set point. The system is presumed to be a linear, open loop stable and known one. The controller parameters are determined by the performance criterion and Fletcher-Powell methods.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.850-860
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• 2023

An industrial control process multiple-input multiple-output (MIMO) coupled system is analyzed in this study as an example of a Loss of Coolant Accident (LOCA) simulation system. Ordinary control algorithms can complete the steady state of the control system and even reduce the response time to some extent, but the entire system still consumes a large amount of energy after reaching the steady state. So a multivariable decoupled energy-saving control method is proposed, and a novel energy-saving function (economic function, Eco-Function) is specially designed based on the active disturbance rejection control algorithm. Simulations and LOCA simulation system tests show that the Eco-function algorithm can cope with the uncertainty of the multivariable system's internal parameters and external disturbances, and it can save up to 67% of energy consumption in maintaining the parameter steady state.

• Journal of Biomedical Engineering Research
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• v.8 no.2
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• pp.245-254
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• 1987

A tuning method for the conventional PID controller in a two-input two-output multivariable system presented. The system is assumed to be no disturbances and modelling errors. Simulation studies show that controller can provide excellent tuning for highly interacting or unfavorable processes.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.808-813
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• 1999

A fuzzy learning method to control an unstable and multivariable system is presented in this paper, Because the multivariable system has generally a coupling effect between the inputs and outputs, it is difficult to find its modeling equation or parameters. If the system is unstable, initial condition rules are needed to make it stable because learning is nearly impossible. Therefore, this learning method uses the initial rules and introduces a cost function composed of the actual error and error-rate of each output without the modeling equation. To minimize the cost function, we experimentally got the Jacobian matrix in the operating point of the system. From the Jacobian matrix, we can find the direction of the convergence in the learning, and the optimal control rules are finally acquired when the fuzzy rules are updated by changing the portion of the errors and error rates.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.7
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• pp.79-86
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• 1992

Recent research on the application of fuzzy set theory to the design of control systems has led to interest in the theory of decomposition of multivariable fuzzy systems. Decomposition of multivariable control rules is preperable since it alleviates the complexity of the problem. However inference error is inevitable because of its approximate nature. In this paper we define an index of applicability which can classify whether the Gupta et. al's method can be applied to multivariable fuzzy system or not. We also propose a modified version of the decomposition which can reduce inference error and improve performance of the system.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.841-846
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• 1990

In this study, a design method to obtain a robust suboptimal regulator for linear multivariable system is presented. This new design method is based on the optimal regulator design method using eigen-structure assignment and it uses additional cost function which represent robustness of the closed loop system. When we design the regulator using pole assignment method for linear multivariable system we have extra degree-of-freedom after assigning desired eigenvalues of the closed loop system in determining the feedback gain. So we assign additional robust suboptimal regulator. In this study we also feedback the system output for more practical applications.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.3
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• pp.257-266
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• 1999

The aim of this paper is to suggest a design method of the optimal multivariable $H_{\infty}$ control system using genetic algorithm (GA). This $H_{\infty}$ control system is designed by applying GA to the optimal determination of weighting functions and design parameter $\gamma$ that are given by Glover-Doyle algorithm which can design $H_{\infty}$ controller in the state space. The first method to do this is that the gains of weighting functions and $\gamma$ are optimized simultaneously by GA with tournament method. And the second method is that not only the gains and $\gamma$ but also the dynamics of weighting functions are optimized at the same time by eA with roulette-wheel method. The effectiveness of this $H_{\infty}$ control system is verified by computer simulation.

• Journal of Power System Engineering
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• v.3 no.4
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• pp.69-78
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• 1999

In the single-input and single-output system, the parameter of plant is scalar polynomial, but in the multiple input and multiple output, it accompanies, being matrix polynomial, the consideration of observable controlability index or problems non-commutation in matrix polynomial as well as degree, and it is more complex to deal with. Therefore, it is thought that a full research on the single-input and single-output system is not sufficient. This paper proposes that problems of minimum variance self-tuning regulator by using numerical calculation example of multivariable system and pole assignment self-tuning regulator.