• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.387-390
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• 1998

In this paper, we suggested a neuro-fuzzy learning algorithm for tuning fuzzy rules, in which a fuzzy system model is of additive-type. Using the method, it is possible to reduce the computation size, since performing the fuzzy inference and tuning the fuzzy rules of each fuzzy subsystem model are independent. Moreover, the efficiency of suggested method is shown by means of a numerical example.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.819-826
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• 1994

This paper deals with a self-tuning fuzzy controller. The fuzzy controller is constructed with linguistic rules which consist of the fuzzy sets. Each fuzzy set is characterized by a membership function. The tuning fuzzy controller has paramenters that are input/output scaling factors to effect control output. In this paper we propose a tuning method for the scaling factor Computer simulations carried out on first-order and second-order processes will show how the present tuning approach improves the transient and the steady-state characteristics of the overall system.The applicability of the proposed algorithm is certified by computer simulation results.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.218-220
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• 1993

This paper deals with a self-tuning fuzzy controller. The fuzzy controller is constructed with linguistic rules which consist of the fuzzy variables and fuzzy sets. Each of fuzzy sets is characterized by a membership function. The tuning fussy controller has paramemters to effect control output. In this paper we propose tuning method for the scaling factor. Computer simulations carried out on a second-order process will show how the present tuning approach improves the transient and steady-state characteristics of the overall system.

• Journal of Advanced Navigation Technology
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• v.20 no.3
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• pp.226-231
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• 2016

Researching and developing mobile robot are quite important. Autonomous driving of mobile robot is important in various working environment. For its autonomous driving, mobile robot detects obstacles and avoids them. Purpose of this thesis is to analyze kinematics model of the mobile robot and show the efficiency of type-2 fuzzy self-tuning PID controller used for controling steering angle. Type-2 fuzzy is more flexible in verbal expression than type-1 fuzzy because it has multiple values unlike previous one. To compare these two controllers, this paper conduct a simulation by using MATLAB Simulink. The result shows the capability of type-2 fuzzy self-tuning PID is effective.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.2
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• pp.55-61
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• 2000

In this paper, we proposed a new PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kc, $\tau$I, $\tau$D by the Ziegler-Nichols formula using the ultimate gain and ultimate period from a relay tuning experiment. We get error and error change of plant output correspond to the initial value and new proportion gain(Kc) and integral time($\tau$I) from fuzzy tunner. This fuzzy tuning algorithm for PID controller considerably reduced overshoot and rise time compare to any other PID controller tuning algorithms. In real parametric uncertainty systems, the PID controller with Fuzzy auto-tuning give appreciable improvement in the performance. The significant properties of this algorithm is shown by simulation In this paper, we proposed a new PID algorithm by the fuzzy set theory to improve the performance of the PID controller.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.480-483
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• 2003

This paper deals with speed control of Sensorless DC servo motor using a FTHGO(FuzEy-Tuning High Gain observer). In this paper, we improved the problem from row speed section, the problem of sensor for detecting speed of motor, using FTHGO(Fuzzy-Tuning High-Gain Observer) with fuzzy control technique which is a class of adaptive control technique. In order to verify the performance of the FTHGO(Fuzzy-Tuning High Gain Observer) which is proposed in this paper, it is proved from the experiment to compare the case with a speed sensor to the case with FTHGO(Fuzzy-Tuning High Gain observer) in the speed control of DC servo motor.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.985-992
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• 2001

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.518-526
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• 2008

The fuzzy self-tuning PID controller is a PID controller with a fuzzy logic mechanism for tuning its gains on-line. In this structure, the proportional, integral and derivative gains are tuned on-line with respect to the change of the output of system under control. This paper deals with two types of fuzzy self-tuning PID controllers, rule-based fuzzy PID controller and learning fuzzy PID controller. As a medical application of fuzzy PID controller, the proposed controllers were implemented and evaluated in a laparoscopic surgery robot system. The proposed fuzzy PID structures maintain similar performance as conventional PID controller, and enhance the position tracking performance over wide range of varying input. For precise approximation, the fuzzy PID controller was realized using the linear reasoning method, a type of product-sum-gravity method. The proposed controllers were compared with conventional PID controller without fuzzy gain tuning and was proved to have better performance in the experiment.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.279-281
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• 2006

In this paper, we study an approach to design a self-tuning Fuzzy-PI controller in HVDC(High Voltage Direct Current) system. In the rectifier of conversional HVDC system, turning on, turning off, triggering and protections of thyristors have lots of problems that can make the dynamic instability and cannot damp the dynamic disturbance efficiently. The above problems are solved by adapting Fuzzy-PI controller for the fire angle control of rectifier.[7] The performance of the Fuzzy-PI controller is sensitive to the variety of scaling factors. The design procedure dwells on the use of evolutionary computing(Genetic Algorithms, GAs). Then we can obtain the optimal scaling factors of the Fuzzy-PI controller by Genetic Algorithms. In order to improve Fuzzy-PI controller, we adopt FIS to tune the scaling factors of the Fuzzy-PI controller on line. A comparative study has been performed between Fuzzy-PI and self-tuning Fuzzy-PI controller, to prove the superiority of the proposed scheme.

• 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.