• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.190-192
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• 1987

In the paper an multivariable decoupling self-tuning algorithm is proposed for controller design, by specifying the closed-loop behaviour of the system in the form of a reference model, so that the controller parameters can be estimated on-line as the process development. The effectiveness of this algorithm in controlling multivariable systems is demonstrated by simulation example in spite of the usual implementation problems of self-tuning controllers.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.21-28
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• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.7
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• pp.66-77
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• 1997

This paper presents an automatic tuning method for parameters of a multivaiable self-tuning velocity-type PID controller which adapts to changes in the system parameters with time delays and noises. The velocity-type PID control structure is determined in the process of minimizing the variance of the auxiliarly output, and self-tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optiminzing the design parameters of the controller. The proposed PID type multivariable self-tuning method is simple andeffective compared with other esisting multivariable self-tuning methods. Computer simulation has shown that the proposed algorithm is beter than the trial-and-error method in the tracking performance.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.22-33
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• 1999

This paper presents the method for the automatic tuning of a design weighting polynomial parameters of a generalized minimum-variance stochastic ultivariable self-tuning controller which adapts to changes in the higher order nonminimum phase system parameters with time delays and noises. The self-tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing the design weighting polynomial parameters of the controller. The proposed multivariable self-tuning method is simple and effective compared with pole restriction method. The computer simulation results are presented to adapt the higher order multivariable system with nonminimum phase and with changeable system parameters.

• Proceedings of the KIEE Conference
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• 1986.07a
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• pp.132-135
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• 1986

This paper shows that using the multivariable controller reduces the magnitude of fluctuations of the control signals which result improved control of the steam generator outputs. Comparison of the performance of the multivariable weighted adaptive controller(MWAC) with the performance of the existing PI controller and the self_tuning controller/1/, when the system goes through a transient mode, shows that the out-puts stay closer to their set points when they are controlled by the adaptive controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.25-28
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• 1995

A Practical application of self-tuning fuzzy controller to a multi-input multi-output complex system of a vehicle engine is investigated. The ovjective is to design a controller to improve the transient performance in torque and RPM mode changes. For the performance improvement in the multivariable comples system, the self-tuning function of internal parameters is essential and practical. The measured output variables using different control schemes are compared the advanteges of the self-tuning fuzzy logic controller are better output performances and the effectiveness in the controller design using many parameters.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.115.5-115
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• 2001

This paper suggests that the immune algorithm can effectively be used in tuning of a multivariable system. Then artificial immune network always has a new paraller decentralized processing mechanism for various situations, since antibodies communication to each other among different species of antibodies/B-cells through the simulation and suppression chains among antibodies that form a large-scaled network. In addition to that, the structure of the network is not fixed, but varies continuously. That is, the artificial immune network flexibly self-organizes according to dynamic changes of external environment (meta-dynamics function). However, up to the present time, models based on the conventional crisp approach ...

• Proceedings of the KAIS Fall Conference
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• 2010.11a
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• pp.341-343
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• 2010

The parameters of PID controller should be readjusted whenever system character change. In spite of a rapid development of control theory, this work needs much time and effort of expert. In this paper, to resolve this defect, after the sample of parameters in the changeable limits of system character is obtained, these parametrs are used as desired values of back propagation learning algorithm, also neural network auto tuner for PID controller is proposed by determing the optimum structure of neural network. Simulation results demonstrate that auto-tuning proper to system character can work well.