• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.53-55
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• 2004

This paper deals with speed control of AC servo motor using a Adaptive fuzzy high gain observer. In this parer, the gain of the observer is properly set up using the fuzzy control and adaptive high gain observer that have a superior transient characteristic and is easy to implement compared the existing method is designed. In order to verify the performance of the Adaptive fuzzy high gain observer which is proposed in this paper, it is compared estimate performance of High-gain Observer and Adaptive High Gain Observer with the computer simulation. Effectiveness of the proposed high gain observer is proved from the experiment to compare the case with a speed sensor to the case with Adaptive fuzzy high gain observer in the speed control of AC servo motor.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.384-388
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• 1999

This paper describes an approach for synthesizing a modularized gain scheduled PD type fuzzy logic controller(FLC) of nonlinear magnetic bearing system where the gains of FLC are on-line adapted according to the operating point. Specifically the systematic procedure via root locus technique is carried out for the selection of the gains of FLC. Simulation results demonstrate that the proposed gain scheduled fuzzy logic controller yields not only maximization of stability boundary but also better control performance than a single operating point (without gain scheduling)fuzzy controller.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1997.03a
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• pp.235-246
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• 1997

Hybrid fuzzy gain scheduling controller is composed of a PD control and a fuzzy control for taking the advantage of each scheme. The key structure of the hybrid fuzzy gain scheduling control scheme is so called a switch which calculates weighting values between the fuzzy controller and the PD controller. However, due to the requirement of the switch , the hybrid fuzzy gain scheduling control scheme needs extra fuzzy logic processing, thus the structure is complicated. and requires more calculation time. To eliminate the drawbacks, a new hybrid fuzzy gain scheduling control scheme is proposed in this paper. In the proposed scheme, the membership function, for calculating of weithting value, and the input and output membership functions are combined. Thus the proposed hybrid scheme does not require switch for calculation of weighting value, and as a result, the calculation time is faster and the structure is more simple than the existing hybrid controller. Computer simulation results for an inverted pendulum model under Pole-Placement PID controller, fuzzy gain scheduling controller,existing hybrid controller , and proposed hybrid controller are compared to demonstrate the good property of the proposed hybrid controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.47-52
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• 2012

This paper focuses on discrete-time sliding mode control with SIIM fuzzy adaptive switching gain. The adaptive switching gain is calculated using the simplified indirect inference fuzzy logic. Two fuzzy inputs are the normal distance from the present state trajectory to the switching function and the distance from the present state trajectory to the equilibrium state. The fuzzy output $f_{out}$(k) out f k is used to adjust the speed the adaptation law depending on the location of the state trajectory. The simulation results showed that the proposed method had no chattering in case of uncertain parameter without disturbance. Moreover the convergent rate of the switching gain was faster and more stable even in case of disturbance.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.4
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• pp.113-118
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• 2017

As the demand for industrial robots and Automated Guided Vehicles (AGVs) increases, higher performance is also required from them. Fuzzy controllers, as part of an intelligent control system, are a direct control method that leverages human knowledge and experience to easily control highly nonlinear, uncertain, and complex systems. This paper uses a $LabVIEW^{(R)}-based$ fuzzy controller with gain scheduling to demonstrate better performance than one could obtain with a fuzzy controller alone. First, the work area was set based on forward kinematics and inverse kinematics programs. Next, $LabVIEW^{(R)}$ was used to configure the fuzzy controller and perform the gain scheduling. Finally, the proposed fuzzy gain scheduling controller was compared with to controllers without gain scheduling.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.465-465
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• 2000

The paper presents the fuzzy control technique to adjust the gain schedule in the fuzzy controller. The micro computer is designed to the fuzzy controller to execute the proportional gain with the data of the error and speed command. The gain schedule is the fuzzy set which execute based on the fuzzy rule. The gain schedule from the fuzzy controller is fed to the sinusoidal pulse width modulation (SPWM) inverter for control the response and speed of the induction motor. The induction motor coupling to the DC motor and tachogenerator which DC motor as a load. The test result of the fuzzy control technique in the open loop control, it provides a good response and in the closed loop control it can control speed in the any condition of load design

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.192-194
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• 1996

This paper propose an optimal scaling gain tuning method of the fuzzy PI controller using Genetic Algorithm(GA). Scaling gains can reflect the control resolution and fuzziness of input/output variables. By the scaling gain method, the design of a fuzzy logic controller(FLC) can be simplified without affecting the system performance in comparison with multi-decision table method. In designing a fuzzy logic controller, the analytic approach method for the optimization is unavailable. Therefore GA is excellent optimization algorithms for scaling gain tuning. Using this optimal scaling gain tuning method, a good performance can be achieved both in transient and steady state.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.141.1-141
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• 2001

This paper describes an approach for synthesizing a modularized gain scheduled PD type fuzzy logic controller(FLC) for a high maneuverable aircraft system, where the gains of FLC are on-line adapted according to the flight condition. Specially, the systematic procedure via root locus technique is carried out for the sellection of the gains of FLC. Simulation results demonstrate that the proposed gain scheduled fuzzy logic controller yields better control performance than the normal (without gain scheduling) fuzzy controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.188-199
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• 2014

Although the fuzzy logic controller is superior to the proportional integral derivative (PID) controller in motor control, the gain tuning of the fuzzy logic controller is more complicated than that of the PID controller. Using mathematical analysis of the proportional derivative (PD) and fuzzy logic controller, this study proposed a design method of a fuzzy logic controller that has the same characteristics as the PD controller in the beginning. Then a design method of a fuzzy logic controller was proposed that has superior performance to the PD controller. This fuzzy logic controller was designed by changing the envelope of the input of the of the fuzzy logic controller to nonlinear, because the fuzzy logic controller has more degree of freedom to select the control gain than the PD controller. By designing the fuzzy logic controller using the proposed method, it simplified the design of fuzzy logic controller, and it simplified the comparison of these two controllers.