• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.309-314
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• 2009

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.40-45
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• 2016

This paper presents the neuro-fuzzy control method for balancing a two-wheel mobile robot. A two-wheel mobile robot is built for the experimental studies. On-line learning algorithm based on the back-propagation(BP) method is derived for the Takagi-Sugeno(T-S) neuro-fuzzy controller. The modified error is proposed to learn the B-P algorithm for the balancing control of a two-wheel mobile robot. The T-S controller is implemented on a DSP chip. Experimental studies of the balancing control performance are conducted. Balancing control performances with disturbance are also conducted and results are evaluated.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.480-480
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• 2000

In this paper, we proposed a fuzzy-neuro controller to control the speed of wound rotor induction motor with slip energy recovery. The speed is limited at some range of sub-synchronous speed of the rotating magnetic field. Control speed by adjusting resistance value in the rotor circuit that occurs the efficiency of power are reduced, because of the slip energy is lost when it passes through the rotor resistance. The control system is designed to maintain efficiency of motor. Recently, the emergence of artificial neural networks has made it conductive to integrate fuzzy controllers and neural models for the development of fuzzy control systems, Fuzzy-neuro controller has been designed by integrating two neural network models with a basic fuzzy logic controller. Using the back propagation algorithm, the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance on-line. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The difficulty of fine-tuning the scale factors and formulating the correct control rules in a basic fuzzy controller may be reduced using the proposed scheme. The scheme is applied to the control speed of a wound rotor induction motor process. The control system is designed to maintain efficiency of motor and compensate power factor of system. That is: the proposed controller gives the controlled system by keeping the speed constant and the good transient response without overshoot can be obtained.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.6
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• pp.61-71
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• 2003

In the study of control technique for a turbojet engine model, the Takagi-Sugeno fuzzy logic controller has been designed based on the model identification by the well designed PI controlled system through T-S neuro-fuzzy inference system. To enhance this designed controller, those procedures are proposed that certainty factors are adopted to each rule of objective groups which are classified by the fuzzy C-Means algorithm and the satisfaction degrees are matched to meet the objectives. This proposed technique shows its feasibility by upgrading performances of the previously well-designed T-S fuzzy controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.289-292
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• 2005

본 논문에서는 클러스터링과 뉴로-퍼지 모델링을 동시에 실시하는 학습 기법을 제안하였다. 클러스터링을 이용하여 뉴로-퍼지 모델링을 실시하는 일반적인 경우, 클러스터링 학습을 실시한 후 학습된 파라미터를 뉴로-퍼지 모델의 초기 파라미터로 설정하고 모델을 다시 학습하는 방법을 취한다. 즉 클러스터링에서 클러스터의 수를 구하고 파라미터를 최적화함으로써 초기 구조동정과 파라미터 동정을 실시하며 이를 다시 뉴로-퍼지 모델에서 세부적인 파라미터 동정을 실시하는 것이다. 또한 모델에서의 학습은 출력데이터의 오차를 이용한 오차미분기반 학습으로 전제부 소속함수 파라미터를 수정하는 방법을 이용한다. 이 경우 클러스터링의 영향과 모델의 영향이 각각 별개로 고려될 수 있다. 따라서 본 논문에서는 클러스터링을 전제부 소속함수로 부여하고 클러스터링의 학습에 뉴로-퍼지 모델을 이용하면서 또한 모델의 학습에 클러스터링을 직접 적용하는 클러스터링 기반 뉴로-퍼지 모델링을 제안하였으며 이 경우 클러스터링의 학습과 모델의 학습이 동시에 이루어지며 뉴로-퍼지 모델에서 클러스터링의 효과를 직접적으로 확인할 수 있다. 제안된 방법의 유용성을 시뮬레이션을 통하여 보이고자 한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.405-414
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• 2011

The adaptive network-based fuzzy inference system (ANFIS) which had a success for time series prediction and system control was applied for modeling the hourly runoff in the Gapcheon watershed. The ANFIS used the antecedent rainfall and runoff as the input. The ANFIS was trained by varying the various simulation factors such as mean areal rainfall estimation, the number of input variables, the type of membership function and the number of membership function. The root mean square error (RMSE), mean peak runoff error (PE), and mean peak time error (TE) were used for validating the ANFIS simulation. The ANFIS predicted runoff was in good agreement with the measured runoff and the applicability of ANFIS for modelling the hourly runoff appeared to be good. The forecasting ability of ANFIS up to the maximum 8 lead hour was investigated by applying the different input structure to ANFIS model. The accuracy of ANFIS for predicting the hourly runoff was reduced as the forecasting lead hours increased. The long-term predictability of ANFIS for forecasting the hourly runoff at longer lead hours appeared to be limited. The ANFIS might be useful for modeling the hourly runoff and has an advantage over the physically based models because the model construction of ANFIS based on only input and output data is relatively simple.