• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.4
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• pp.1-7
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• 2002

QFT is a very practical design technique that emphasizes the use of feedback for achieving the desired system performances in despite of plant uncertainties and disturbances. The loop shaping procedure of QFT is employed to design the robust controller, until the desired bounds are satisfied. This paper presents an optimization algorithm for designing PID controller using the loop shaping of QFT. The proposed method identifies the parameter vector of PID controller from a linear system that develops from rearranging the two dimensional system matrices and output vectors obtained from the QFT bounds. The feasibilities of the suggested algorithm are illustrated with a turbine speed control problem.

• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.813-819
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• 2009

Designing actuator controllers of aircraft, which control aileron, flap, elevator and so on, is quiet difficult, because they have time variant nonlinear mechanical structures and also have many kinds of disturbances which are not been able to model easily. This paper reports about the performance of Fuzzy Auto gain tuning Control algorithm applied unmaned aerial vehicle. Fuzzy Auto gain tuning PID control uses PID control and Fuzzy control, therefore It can be applied very easily and it also has advances of PID control. It can control a unmaned aerial vehicle actuators adaptively even though the designer does not have enough information of plant.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.530-535
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• 2011

This paper deals with the precise temperature control of the heater used at a weaving thread or a drawn process. For precise temperature control, we suggest a design method that is auto-tuning IMC-PID controller using an optimization method uDEAS. For this method, we model the roll heater from the measurement data and we automatically tune the low pass filter value of IMC-PID controller that satisfies stability and conrol performance. Finally, we implement the designed controller using DSP kit.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.1
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• pp.49-56
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• 2001

This paper presents a new approach to the problem based on neural network methods. Instead of using general controllers, neural networks PID control are used to control AC servo motor. The most popular and widely used control method in servo system control loops is PID type. PID controller has the features of simple structure, stability and reliability. But it has limitations in complex system control and can not remain above virtues under the conditions of parameters uncertain and environment uncertainties. AC servo motor controller is designed for drive of the cartesian coordinate type for machine tools.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.26-34
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• 2015

In this paper, a fuzzy compensated PID control system is proposed for formation control of mobile robots. The control system consists of a kinematic controller based on the leader-follower approach and a dynamic controller to handle dynamics effects of mobile robots. To maintain the desired formation of mobile robots, the dynamic controller is equipped with a PID controller; however, the PID controller has poor performance in nonlinear and changing environments. In order to improve these problem, we applied the additional fuzzy compensator. Finally, the proposed control system has been evaluated through computer simulation to demonstrate the improved results.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.86-86
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• 2000

This paper describes a neural network based fuzzy type PID control scheme. The PID controller is being widely used in industrial applications. however, it is difficult to determine the appropriate PID gains for (he nonlinear system control. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based fuzzy type PID controller whose scaling factors were adjusted automatically. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The result of practical experiment on the magnetic levitation system, which is known to be hard nonlinear, showed the proposed controller's excellent performance.

• Journal of the Semiconductor & Display Technology
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• v.1 no.1
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• pp.21-28
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• 2002

This paper presents a dynamic modeling and a robust PID controller design process for the wire bonder head assembly. For modeling elements, the system is divided into electrical part, magnetic part, and mechanical part. Each part is modeled using the bond graph method. The PID controller is used for high speed/high accuracy position control of the wire bonder assembly. The Taguchi method is used to obtain the more robust PID gain combinations than conventional one. This study makes use of an L18 array with three parameters varied on three levels. Results of simulations and experimental show that the designed PID controller provides a improved ratio of signal to noise and a reduced sensitivity improved to the conventional PID controller.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.521-527
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• 2002

This paper presents a dynamic modeling and a robust PID controller design process for the wire bonder head assembly. For the modeling elements, the system is divided into electrical system, magnetic system, and mechanical system. Each system is modeled by using the bond graph method. The PID controller is used for high speed/high accuracy position control of the wire bonder assembly. The Taguchi method is used to evaluate the more robust PID gain combinations than conventional one. This study makes use of an L18 array with three parameters varied on three levels. Computer simulations and experimental results show that the designed PID controller provides more improved signal to noise ratio and reduced sensitivity than the conventional PID controller.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.19-26
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• 2005

This paper is concerned with the design of a predictive PID controller which has similar features to the model-based predictive controller. A PID type control structure is defined, which includes prediction of the outputs and the recalculation of new set points using the future set point data. The optimal values of the PID gains are precalculated using the values of gains calculated from an unconstrained generalized predictive control algorithm. Simulation studies demonstrate the performance of the proposed controller and the results are compared with the conventional PID and fuzzy control algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.286-295
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• 2007

This paper presents an intelligent PID control for stochastic systems with nonstationary nature. We optimally determine parameters of a PID controller through learning algorithm and propose an online PID control to compensate system errors possibly occurred in realtime implementations. A dynamic Bayesian network (DBN) model for system errors is additionally explored for making decision about whether an online control is carried out or not in practice. We apply our control approach to traffic control of Transmission Control Protocol (TCP) networks and demonstrate its superior performance comparing to a fixed PID from computer simulations.