• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.60-65
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• 2000

We propose a new method to deal with the optimal gain self-tuning of the PID controller which is used to industrial process control in various fields. First of all, in this method, first order delay system which was modeled from the unit step response of the system is Pade-approximated, then initial values are determined by the Ziegler-Nickels method. Finally, we can find the parameters of Pm controller so as to maximize the fuzzy inferencl 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 time of the system.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.6
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• pp.57-64
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• 2001

This study presents an self tuning regulator(STR) to improve the air-fuel ratio control of performance of gasoline engine. The STR is designed based on the nonlinear dynamic engine model, and the performance of the STR is evaluated through the simulation and experiments. The STR shows better performance than a conventional PI controller in terms of the response time and disturbance rejection. Since the STR has less calculation load than the complex nonlinear controller, this algorithm can be easily applied to on-board engine controller.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.3
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• pp.25-34
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• 1992

This paper proposes a Self-Tuning control algorithm for tracking the reference trajectory by measuring the end-point of robot manipulator whose links are light and flexible, and the performance of it is tested through the computer simulation. As an object of system, a flexible robot manipulator with two-links is considered and an assumed mode shape method including gravity force is adopted to analyze the vibration modes for each links and dynamics equation is derived. The controller is designed as a combined form which consists of dynamic feedforward compensator and self-tuning feedback controller. The one supplies nominal torque and the other supplies variational torque to manipulator. Apart from the, K-incremental predictor is also proposed in order to eliminate the offset error. and it shows that the result of simulation adapted well to load change and rapid velocity.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.3
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• pp.247-255
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• 1989

This paper proposes an error model with integral action and a pole-place-ment self-tuning controller for robot manipulators in task coordinates. The controller can reject the offset due to any load disturbance without a detailed description of the robot dynamics. The error model parameters are estimated by the recursive least square identification algorithms, and controller parameters are determined by the pole-placement method. A computer simulation study has been conducted to demonstrate the performance of the proposed control system in task coordinates for a 3-joint and 2-link spatial robot manipulator with payload.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.4
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• pp.52-57
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• 2003

A PID parameter tuning method was presented by the fuzzy singleton inference, based on step response-shaping of plant and experience knowledge of expert. The parameter-tuning has tow levels. The higher level determines modified coefficients for the controller based on operator's tuning know-how for characteristics of plant which can not be modeled. The lower level determines specified coefficients based on characteristics of response by Ziegler-Nickel's bounded sensitivity method. The last level parameters tuning of a PID controller is adjusted which the modified and specified coefficients makes adjustment rule, and is adjusted the proper value to each parameters by fuzzy singleton inference. Moreover, proposed the tuning method can reflex exporter knowledge and operator's tuning know-how and fuzzy singleton inference is rapidly operated.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.1004-1007
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• 1992

In this paper, we present a solution to the PID tuning problem by optimizing a GPC(General Predictive Control) criterion. The PID structure is ensured by constraning the parameters to a feasible set defined by the discrete-time Euler approximation of the ideal continuous-time PID controller. The algorithm is ectended by incorporating heuristic rules for selection of the significant design parameters. The algorithm has been successfully tested and some results are prewented.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.41-44
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• 1988

This paper represents the study of an effective self-tuning PID control for a robot manipulator to track a reference trajectory in spite of the presence of nonlinearities and parameters uncertainties in robot dynamic models. In this control scheme, an error model of the manipulator is established, for the first time, by difference between joint reference trajectory and tracked trajectory. It's model Parameters are estimated by the recursive least-square identification algorithm, and classical controller parameters are determined by pole placement method. A computer simulation study was conducted to demonstrate performance of the proposed self-tuning PID control in joint-based coordinates for a robot with payload.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.2 s.245
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• pp.105-111
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• 2006

This paper describes a Neural Network based PID control scheme for pneumatic NC axes. Pneumatic systems have inherent nonlinearities such as compressibility of air and nonlinear frictions present in cylinder. The conventional PID controller is limited in some applications where the affection of nonlinear factor is dominant. A self-excited oscillation method is applied to derive the dynamic design parameters of linear model. The gains of PID controller are determined using a self tuning scheme. The experiments of a trajectory tracking control using the proposed control scheme are performed and a significant reduction in tracking error is achieved by comparing with those of a PID control.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.248-252
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• 2003

This paper has a control method proposed for the effective self-tuning fuzzy speed control based on neural network of the induction motor indirect vector control. The vector control of an induction motor provides the decoupled control of the rotor flux magnitude and the torque producing current to performance is desirable. But, the drive performance often degrades for the machine parameter variations and its condition give rise to coupling of flux and torque current. The fuzzy speed control of an induction motor has the robustness about machine parameter variations compared with conventional PID speed control in a way. That proved to be some waf from the true. The purpose of this paper is to improve the adaptation by offering self-turning function to fuzzy speed controller. In this paper, the adaptive mechanism of fuzzy speed control in used ANN(Artificial Neural Network) technique is applied in an IFO induction machine drive, such that the machine can follow a reference model (an ideal field oriented machine) to achieve desired speed. In this paper proved the self-turning method of fuzzy controller has the robustness about parameter variation and the wide range of adaptation by simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.925-931
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• 2016

The BLDC motor is used widely in industry due to its controllability and freedom from maintenance because there is no mechanical brush in the BLDC motor. Furthermore, it is suitable for high-speed applications, such as compressors and air blowers. For instance, for a compressor with a small impeller due to miniaturizing, the BLDC motor has to rotate at a very high speed to maintain the compression ratio of the compressor. Typically, to reach an ultra-high speed, airfoil bearings must be used in place of ball bearings because of their friction. Unfortunately, the characteristics of airfoil bearings change drastically depending on the revolution speed. In this paper, a BLDC motor with airfoil bearings is controlled with a PID controller. To analyze and determine the PID coefficients, the relay-feedback method is used. Additionally, for adaptive control, a fuzzy logic controller is used. Furthermore, the auto-tuning and self-tuning techniques are combined to control the BLDC motor. The proposed method is able to control the airfoil-bearing BLDC motor efficiently.