• Journal of the Korea Convergence Society
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• v.4 no.2
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• pp.43-50
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• 2013

Sliding mode control(SMC) are carried out in this literature. And to make the controllers perform better, fuzzy logic was chosen,it makes PID controller auto-tuning parameters and reduced the chattering problem of sliding mode control. Since SMC take error and derivative of error as inputs, after comparison some results are obtained.PID controller response faster yet sliding mode control is much steadier. However certain problems cannot be ignored that the chattering phenomenal cannot be reduced entirely and this motion may hurt the machine; this project only considered a simple system, there is no guarantee PID can work as well as in this case for a much more complex system. MATLAB simulink was the main approach to obtain the performance of the two controllers: to observe the control output of the two controllers, electric circuit and special controllers are designed and tested in MATLAB.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.504-506
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• 1999

In this paper, we propose a method which control parametric uncertainty systems using PID controller by fuzzy auto tuning. We get the error and the error change rate of plant output correspond to the initial value of parameter using the Ziegler-Nickols tuning and determine the new proportional gain$(K_p)$ and the integral time $(T_i)$ from fuzzy tuner by the error and error change rate of plant output as a membership function of fuzzy theory. The Fuzzy Auto-tuning algorithm for PID controller operate to adapt variable parameter of plant in parametric uncertainty systems. It is shown this method considerably improve the transient response at computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.847-852
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• 1993

In this paper, we present a recursive algorithm for the auto-tuning of PID controllers by optimizing a GPC criterion. Also, we develop an intelligent PID controller by combination of a recursive algorithm together with a supervisor, that allows to adjust the main controller parameters (prediction horizon, control weighting, sample time etc.) using some simple rules which is mainly built up through relay tuning experiments. The intelligent PID controller has been implemented successfully on an IBM PC/AT and some simulation results are presented.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.5
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• pp.485-493
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• 1990

The idea of expert control is to incorporate a rule based expert system in a feedback control system. In this paper, we present some heuristic rules based on relay experiment for the choice of controller structure and the setting of the controller parameters. Heuristic rules are used as an element of the feedback loop in an auto-tuning PID controller. The algorithms are coded in a form which is as pure as possible and the heuristic logic is implemented with the rules. This paper reports an implementation of an expert controller on microcomputer-based system, including an industrial programmable controller.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.8 no.3
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• pp.64-70
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• 1994

We propose a new method to deal with optimal auto-tuning of the PID controller which is used to process control in various fields. First of all, in this method, 1st order system which was modeled from the unit step response of the system is Pade-approximated, then initial values are determined by the Ziegler-Nichols method. Finally, we can find the parameters of PID controller so as to maximize the fuzzy criterion function which includes the maximum overshoot, damping ratio, rising time and settling time. The Proposed method also shows good adaptability for variations in characteristics and dead m e of the system.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1004-1007
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• 1996

Various auto-tuning methods using relay feedback are presented recently. They are composed of the consecutive procedures identifying Nyquist critical point using relay feedback and designing PID controller by one point of Nyquist plot mapping. This paper suggests a strategy to get the knowledges of Nyquist critical point and the neighborhood point of it using relay feedback. The parameters of PID controller are established by dominant pole approximation based on these knowledges. Designers can make use of the damping ratio as a time domain specification. So design flexibilities are taken in view of stability and performance of the system response considering practical system condition.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.36-42
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• 1996

In this paper, we proposed a PID controller, which could control unknown plants using Artificial Neural Network(ANN) for auto-tuning of the PID parameters. In the proposed algorithm, the parameters of the controller were adjusted to reduce the error of the controlled plant. In this process, the sensitivity between input and output of the unknown plant was needed. So, in order to obtain this sensitivity, the ANN's learnig ability was used. Computer simualtions were performed for the regulation problems, and the results were compared with those of Ziegler-Nichols PID controller. As a result, it was shown that the proposed algorithm outperformed Ziegler-Nichols controller in rise time, overshoot, undershoot, and setting time.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.195-195
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• 1999

This paper is Proposed a new method to deal with the optimized auto-tuning for the PID controller which is used to the process-control in various fields. First of all, in this method, 1st order delay system with dead time which is modelled from the unit step response of the system is Pade-approximated, then initial values are determined by the Ziegler-Nickels method. So we can find the parameters of PID controller so as to minimize the fuzzy criterion function which includes the maximum overshoot, damping ratio, rising time and settling time. Finally, after studying the parameters of PID controller by Backpropagation of Neural-Network, when we give new K, L, T values to Neural-Network, the optimized parameter of PID controller is found by Neural-Network Program.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.7 no.4
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• pp.42-50
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• 1993

This paper was proposed drive strategy Induction Motor using the fuzzy algorithm of auto-tuning PID control. In fuzzy control, the 1M driving system is controlled in trial and error without the mathematical modeling and using fuzzy lookup table the real time control is possible. Also, ,dividing the fuzzy rules in several zone, the stability and response of the 1M driving system is improved. The parameters of 1M are varying according to the environmental conditions, the variance of the parameters is affected with the driving characteristics of 1M. Using the fuzzy algorithm of the driving system which has the auto tuning control function for high performance, high accuracy of the driving system, a designed and proposed through the comparision with the PID control method and the driving characteristics is reviewed and analyzed.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.458-460
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• 2009

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.