• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1741-1747
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• 2011

Though various techniques have been studied as a way of adjusting parameters of PID controllers, no perfect method of determining parameters is available to date. This paper proposes a new method for enhancing performance of PID controllers by using the characteristics of dual-input describing function (DIDF). In other words, if nonlinear elements with two inputs (DIDF) are connected in series to the plant, the critical point (-1+j0) for Nyquist stability theory can be moved to a position arbitrarily selected on the complex plane by determining necessary coefficients of the DIDF appropriately. This makes the application of the existing conventional PID parameter tuning methods a lot easier, and stability and robustness of the system are improved simultaneously due to the DIDF inserted.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.449-454
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• 1994

This paper describes new PID control methods based on the fuzzy logic. PID gains are retuned after evaluating control performances of transient responses in terms of performance features. The retuning procedure is based on fuzzy rules and reasoning accumulated from the knowledge of experts on PID gain scheduling. For the case that the retuned PID gains result in worse CLDR (characteristics of load disturbance rejection) than the initial gains, an on-line tuning scheme of the set-point weighting parameter is, proposed. This is based on the fact that the set-point weighting method efficiently reduce either overshoot or undershoot without any degradation of CLDR. The set-point weighting parameter is adjusted at each sampling instant by the fuzzy rules and reasoning. As a result, better control performances were achived in comparison with die controllers tuned by the Z-N (Ziegler-Nichols) parameter tuning formula or by the fixed set-point weighting parameter.

• 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.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.621-623
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• 1999

In this paper, Model following PID control system, which is combined PID controller with Model Reference Adaptive Controller, is proposed. To decrease complex and much calculation which is produced in tuning process, the tuning method of parameter with fuzzy algorithm is introduced. Fuzzy algorithm isn't used in the form of controller generally much used, but tuner. Experimental results show that proposed controller has the PID parameter be tuned by fuzzy algorithm. Therefore, We expect model following PID to be operated in the real-time control.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.2
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• pp.1-8
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• 2004

This paper presents an optimal robust LQ-PID controller design method for the second order system with time-delay to meet design specifications. By LQR formulation of the second order system with time-delay, the tuning parameters of PID controller are related by weighting factors Q and R of cost function. The selection of weighting factors Q and R are chosen to satisfy such the design specifications as overshoot and settling time.

• 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.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.338-341
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• 1994

This paper presents a Fuzzy-PID controller based on Fuzzy logic. Up to now PID controller has had the difficulty of obtaining the optimal gain, and Fuzzy controller has had the difficulty of determining scale factor affecting the performance of control. So that a Fuzzy-PID controller is presented here self tuning of the scale factor and optimal gain. The results of simulation show a good performance in comparison with Ziegler-Nichols controller, having the generality of determining the components of scale factor in Fuzzy rule.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.496-500
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• 1993

This paper presents an end-point position control of 1-link flexible robot arm by the PID self-tuning fuzzy algorithm. The governing equation is derived by the extended Hamilton's principle and based on the Bernoullie-Euler beam theory. The governing equation is solved by applying the Laplace transform and the numerical inversion method. The arm is mounted on the translational mechanism driven by a ballscrew whose rotation is controlled by dcservomotor. Tip position is controlled by the PID self-tuning fuzzy algorithm so that it follows a desired position. This paper shows the experimental and theoretical results of tip dispalcement, and also shows the good effects reducing the residual vibration of the end-point.