• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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• pp.555-562
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• 1997

With only the classical PID controller applied to control of a DC motor, good (target) performance characteristic of the controller can be obtained if all the model parameters of DC motor and operating conditions such as external load torque, disturbance, etc. are known exactly. However, in case when some of system parameters or operating conditions are uncertain or unknown, the fixed PID controller does not guarantee good performance, which is assumed with precisely known system parameters and operating conditions. In view of this and the robustness enhancement of DC motor control system, we propose a PID learning controller which consists of a set of learning rules for PID gain tuning and learning of an auxiliary input. The proposed PID learning controller is shown to drive the state of uncertain DC motor system with unknown system parameters and external load torque to the desired one world wide asymptotically. Computer simulation and experimental results are given to demonstrate the effectiveness of the proposed PID learning controller, thereby showing its superiority to the conventional fixed PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.86-89
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• 1986

This paper presents a design methodology for a PID regulator. The parameters of the PID regulator are determined through equivalent structure to the closed-loop system whose feedback gain assigns prescribed eigenvalues of the closed-loop system and minimizes a given performance index.

• Journal of the Korea Convergence Society
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• v.4 no.4
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• pp.31-40
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• 2013

PID auto-tuning controller was designed via fuzzy logic. Typical values such as error and error derivative feedbackwere changed as heuristic expressions, and they determine PID gain through fuzzy logic and defuzzification process. Fuzzy procedure and PID controller design were considered separately, and they are combined and analyzed. Obtained auto-tuning PID controller by Fuzzy Logic showed the ability for less than 3rd order plant control.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.1
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• pp.32-39
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• 1997

One of the most important performance criteria in tuning the gain of position loop controller for CNC machining center is the contour error. In this papre we analyze contour error in the linear and circular interpolations for the axis-matched and mismatched cases. To have small contour errors, it is necessary to set the P gain for each axis to be same. And the D gain should be much smaller than the P gain. Baded on the analysis in the frequency domain, we propose a gain tuning method for the P and PD controllers. We show that the PD controller is better than the P controller. The effectiveness of this method is demonstrated by experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.2
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• pp.403-410
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• 1999

A conventional PID controller has poor performance when it applied to systems with unknown deadzones. To solve this problem, this paper proposes PID controller using two layered-fuzzy logic. The structure of controller is reconstructed with fuzzy compensator and fuzzy tuner on the conventional PID controller. Our proposed control scheme shows superior transient and steady-state performance compared to conventional PID controller. The scheme is robust to variations in deadzone nonlinearities as well as the steady-state gain of the plant. The performance of the developed controller is verified through simulation.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1115-1119
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• 2007

Five control methods (Speed Control, PID Gain Scheduling, Loop Time Control, Simple PID, Switching Control) have been applied to the control of an Inline Co-axial valve by the simulation of AMESim. The simulation results have shown that the speed control method is the most stable and the fastest way to reach to the set point in the simulation of the flow control. Moreover, It has been found that the five control methods have the almost same characteristics in the power consumption, the counter electromotive force, and the motor angular velocity. According to the analysis results, the fast and stable control characteristics of the speed control method is the most suitable for the flow control using a inline co-axial valve with a DC(BLCD) motor.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.1
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• pp.1-6
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• 2013

For regulating generator speed above the rated wind, versatile methods have been published based on PID. However, these methods with the fixed PID gains could not guarantee that the controller works well in the whole area. In this paper, variable PID gain method has been suggested to overcome this problem. The sensitivity of power to blade pitch angle changes according to wind speed. The variable PID gain function has been derived from this sensitivity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.536-544
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• 2011

In this paper, a controller for two wheeled inverted pendulum type robot is designed to have more stable balancing capability than conventional controllers. Traditional PID control structure is chosen for the two wheeled inverted pendulum type robot, and proper gains for the controller are obtained for specified user's weights using trial-and-error methods. Next a neural network is employed to generate PID controller gains for more stable control performance when the user's weight is arbitrarily selected. Through simulation studies we find that the designed controller using the neural network is superior to the conventional PID controller.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.724-729
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• 2012

A fuzzy PID controller design method is proposed for precise robust control of DC-DC buck converters. The PID parameters are determined reflecting on the common control engineering knowledge that transient performances can be improved if the P and I gains are big and the D gain is small at the beginning. Different from the previous fuzzy control design methods, the proposed method requires no defuzzification module and the global stability of the proposed fuzzy control system can be guaranteed. The proposed fuzzy PID controller is implemented by using a low-cost 8-bit microcontroller, and simulation and experimental results are given to demonstrate the effectiveness of the proposed method.