• Journal of the Korean Society for Precision Engineering
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• v.24 no.12
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• pp.57-64
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• 2007

There have been many researches for optimal controllers in multivariable systems, and they generally use accurate linear models of the plant dynamics. Real systems, however, contain nonlinearities and high-order dynamics that may be difficult to model using conventional techniques. Therefore, it is necessary a PID gain tuning method without explicit modeling for the multivariable plant dynamics. The PID tuning method utilizes the sign of Jacobian and gradient descent techniques to iteratively reduce the error-related objective function. This paper, especially, focuses on the role of I-controller when there is a steady state error. However, it is not easy to tune I-gain unlike P- and D-gain because I-controller is mainly operated in the steady state. Simulations for an overhead crane system with dynamic friction show that the proposed PID-LC algorithm improves controller performance, even in the steady state error.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.295-303
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• 2001

The demand for steam generators is increasing in industrial systems in which the design strategy should be implemented for safe and efficient operation of steam generators. It is, however, difficult to design a controller by the conventional method because of the nonlinear dynamics of the steam generator and influences by the set value of disturbance. This paper presents an automatic parameter optimization technique for a multivariable fuzzy controller using evolutionary strategy, At first, we use the steady state information such as a steady state gain matrix(SSGM) and a relative gain matrix(RGM). We can obtain much information on the control inputs and the outputs of the boiler-turbine system from the matrices. In order to determine the structure of the controller by using RGM and SSGM, the fuzzy rules are trained by evolutionary strategy. The good performance of the proposed multivariable fuzzy controller is verified through simulations.

• Journal of the Korean Society for Precision Engineering
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• v.6 no.1
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• pp.33-44
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• 1989

A controller disign method is proposed for multivariable nonlinear stochastic systems with hard nonlinearities such as Coulomb friction, backlash and saturation. In order to take the nonlinearities into account statistical linearization techniques are used. And multi- variable pole placement techniques are applied to design controller for the statistically linearized multivariable systems. The basic concept of the controller design method is to solve two coupled equations, characteristic equation and Lyapunov equation, simultaneously and iteratively for statistically linearized multivariable stochastic systems. An aircraft with saturation serves as a design example. The design example illustrates the influence of nonlinear effects. The results of the analysis are compared to Monte Carlo simulation to test their accuracy.

• 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.

• 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.

• 전기의세계
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• v.24 no.6
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• pp.18-23
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• 1975

대한전기학회로부터 기술해설에 관한 내용의 원고를 의뢰받고 주저했으나 결국은 착수하기로 했다. Multivariable function 이론이 개화하면 응용면에 있어 예측하기 어려울만큼 큰 장래가 약속된다고 믿어져 집필을 했다. 영문을 그대로 실은 대목도 있고 지면절약상 증명은 생략했다. 보완이 될까 생각되어 대표적인 논문들을 참고로 첨부한다. 큰 개발이 요구되는 이 분야에 우리 나라에서도 관심을 갖는 분이 많이 생겨 제기된 문제 해결에 공헌 있기를 바란다.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.760-765
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• 1993

A multivariable learning control is designed in frequency domain. A general to of feedback assisted learning scheme is considered and an inverse model based learning algorithm is derived through convergence analysis in frequency domain. Performance of the proposed control method is evaluated through numerical simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.192-196
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• 1988

This paper presents a feasibility study related to the design of a linear multivariable compensator for a model of boiler-turbine system. The nonlinear dynamics are linearized at a operating condition. At the operating point an LQG/LTR compensator is designed. Simulations are included to illustrate the usefulness of this linear multivariable control law.

• 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.