• 제어로봇시스템학회논문지
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• 제5권8호
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• pp.914-922
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• 1999

A study on the improvement of tracking performance of a 3 DOF planar parallel manipulator is performed. A class of adaptive tracking control sheme is designed using self tuning adaptive fuzzy logic control theory. This control sheme is composed of three classical PD controller and a multi learning type self tuning adaptive fuzzy logic controller set. PD controller is tuned roughly by manual setting a priori and fuzzy logic controller is tuned precisely by the gradient descent method for a global solution during run-time, so the proposed control scheme is tuned more rapidly and precisely than the single learning type self tuning adaptive fuzzy logic control sheme for a local solution. The control performance of the proposed algorithm is verified through experiments.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 B
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• pp.1142-1144
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• 2003

This Paper presents a speed controller based on the adaptive fuzzy tuning method for brushless DC(BLDC) motor drives under load variations. Generally, the speed tracking control systems use PI controller due to its simple structure and easy of design. PI controller, however, suffers from the electrical machine parameter variations and disturbances. In order to improve the tracking control performance under load variations, PI controller of which the parameters are modified during operation by adaptive fuzzy tuning method. This method based on optimal fuzzy logic system has simple structure and computational simplicity. It needs only sample data which is obtained by optimal controller off-line. As the sample data implemented in the adaptive fuzzy system can be modified or extended, a flexible control system can be obtained. Simulation results show the usefulness of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.378-384
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• 1993

In this paper, an intelligent adaptive controller is proposed for the process with unmodelled dynamics. The intelligent adaptive controller consists of the numeric adaptive controller and the intelligent tuning part. The continuous scheme is used for the numeric adaptive controller to avoid the problems occurred in the discrete time schemes. The adaptive controller is adopted to the process with time delay. It is an implicit adaptive algorithm based on GMV using the emulator. The tuning part changes the design parameters in the control algorithm. It is a multilayer neural network trained by robustness analysis data. The proposed method can improve the robustness of the adaptive control system because the design parameters are tuned according to the operating points of the process. Through the simulation, robustnesses are shown for intelligent adaptive controller. Finally, the proposed algorithms are implemented on the electric furnace temperature control system. The effectiveness of the proposed algorithm is shown from experiments.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 춘계학술대회 논문집
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• pp.621-624
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• 2002

The purpose of this paper is a study on getting proper gain set of PID controller which satisfies multi-performance specifications of the control system. The multi-objective optimization method is introduced to evaluate specifications, and the genetic algorithm is used as an optimal problem solver. To enhance the performance of genetic algorithm itself, adaptive technique is included. According to the proposed method in this paper, finding suitable gain set can be more easily accomplishable than manual gain seeking and tuning.

• 대한전기학회논문지
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• 제37권9호
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• pp.655-662
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• 1988

An adaptive control scheme has been recognized as an effective approach for a robot manipulator to track a deired trajectory in spite of the presence of nonlinearies and parameter uncertainties in robot dynamic models. In this paper, an adaptive control scheme for a robot manipulator is proposed to design the self-tuning controller which controls the extended linearized perturbaton model via the pole placement, and this control. The feasibility of the controller is demonstrated by the simulation about position control of a three-link manipulator with payload and parameter uncertainty.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.690-692
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• 1999

In this thesis, We implement the controller system only using the neural network to identify the plant characteristics with keeping the PID controller structure. The neural network has learned by the adaptive learning rates that has suggested by Chao-Chee Ku and the DBP algorithm. We proposed the on-line tuning algorithm about the unknown plant using the adaptive tuning technique. As a result of executing the parameters has tuned from the initial value to more suitable ones and the output of the Plant has improved and also it is appeared that the convergence is guaranteed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.404-406
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• 1998

In this paper, we propose a scaling factor tuning method to improve the performance of fuzzy controller. Tuning rules and reasoning are utilized on-line to determine the scaling factors based on absolute value of the error and its difference. A adaptive evolutionary computation (AEC) is used to search for the optimal tuning rules that will maximize the fitness function. Finally, the proposed fuzzy controller is applied to the angular stabilization of an inverted pendulum.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.442-442
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• 2000

The intelligent auto-tuning method fur a strongly stable adaptive control system of a DC motor with great load inertia variation is proposed. The stable characteristic polynomial that is designed by an optimal servo is specified for the adaptive pole placement control system. The appropriate adaptive control system can be derived, by adjusting automatically the weight of a performance criterion in optimal control by means of the fuzzy inference on the basis of the stability index.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.236-241
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• 1988

This note considers the class of controllers with integral action which arise directly from appropriate system models. Via internal model principle approach, a corresponding class of self-tuning controller is shown to have both integral action in controller and offset removal in the tuning algorithm. The key idea is to constrain the estimator in each step in order to ensure that dc gain of feedforward and feedback polynomial of adaptive controller are always equal, thus allowing the loop integrator to work properly.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.32-35
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• 1987

An adaptive control scheme has been recognized as an effective approach for a robot manipulator to track a desired trajectory in spite of the presence of nonlinearties and parameter uncertainties in robot dynamic models. In this paper, an adaptive control scheme for a robot manipulator is proposed to design the self-tuning controller which combines the pole placement with the extended linearized perturbation model. And this control scheme has two components: a feadforward control and a feedback compensation control. Based on this, the controller is demonstrated by the simulation about position control of a three-link manipulator with payload and parameter uncertainty.