• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.570-572
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• 2005

This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.611-618
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• 2000

This paper presents a shape classification method of dynamic image based on adaptive fuzzy inference. It describes the design scheme of fuzzy inference algorithm which makes it suitable for low speed systems such as conveyor, uninhabited transportation. In the first Discrete Wavelet Transform(DWT) is utilized to extract the motion vector in a sequential images. This approach provides a mechanism to simple but robust information which is desirable when dealing with an unknown environment. By using feature parameters of moving object, fuzzy if - then rule which can be able to adapt the variation of circumstances is devised. Then applying the implication function, shape classification processes are performed. Experimental results are presented to testify the performance and applicability of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.485-488
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• 1993

Fuzzy Logic Control immitating human decision making process is a novel control strategy based on expert's experience and knowledge and many process designers are developing its applications. But it is difficult to obtain a set of ruler from human operators. And there is a limitation on adjusting to environmental changes. In this paper, we proposed adaptive fuzzy algorithm to overcome these difficulties using weights added to the rules. To verify the validity of this control strategy, we have implemented this algorithm for a DC servo motor with PD-type fuzzy controller.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.363-369
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• 2002

In this paper, it could improved on the arbitrary nonlinear function learning approximation which have the wavelet neural network based on Adaptive Neuro-Fuzzy Inference System(ANFIS) and the multi-resolution Analysis(MRA) of the wavelet transform. ANFIS structure is composed of a bell type fuzzy membership function, and the wavelet neural network structure become composed of the forward algorithm and the backpropagation neural network algorithm. This wavelet composition has a single size, and it is used the backpropagation algorithm for learning of the wavelet neural network based on ANFIS. It is confirmed to be improved the wavelet base number decrease and the convergence speed performances of the wavelet neural network based on ANFIS Model which is using the wavelet translation parameter learning and bell type membership function of ANFIS than the conventional algorithm from 1 dimension and 2 dimension functions.

• Advances in Computational Design
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• v.5 no.3
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• pp.233-260
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• 2020

The main purpose to introduce model based controller in proposed control technique is to provide better and fast learning of the floating dynamics by means of fuzzy logic controller and also cancelling effect of nonlinear terms of the system. An iterative adaptive dynamic programming algorithm is proposed to deal with the optimal trajectory-tracking control problems for autonomous underwater vehicle (AUV). The optimal tracking control problem is converted into an optimal regulation problem by system transformation. Then the optimal regulation problem is solved by the policy iteration adaptive dynamic programming algorithm. Finally, simulation example is given to show the performance of the iterative adaptive dynamic programming algorithm.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.4
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• pp.37-43
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• 1993

As an approach to develope the intelligent control scheme, this paper will propose an adaptive neural-fuzzy control scheme. The proposed neural-fuzzy control system, which consists of the Fuzzy-Neural Controller(FNC) and Model Neural Network(MNN), has two important characteristics of adaptation and learning. The error back propagation algorithm has been adopted as a learning technique.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2879-2881
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• 1999

This paper presents the adaptive Active Noise Control(ANC) system using the Neuro-Fuzzy controller. In general, the character of noise is time-varing and nonlinear Thus controller must have the adaptivness so that applied in Active Noise Control system to cancel the noise. This paper propose the Neuro-Fuzzy controller trained with back-propagation teaming algorithm to optimize the parameters of controller The objects of this paper are cancel the noise, extract the original(speech) signal polluted by noise and design the Neuro-Fuzzy controller.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.414-422
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• 2002

In this paper, we propose a new method of adaptive neuro-fuzzy control using CFCM(Conditional Fuzzy c-means) clustering and fuzzy equalization method to deal with adaptive control problem. First, in the off-line design, CFCM clustering performs structure identification of adaptive neuro-fuzzy control with the homogeneous properties of the given input and output data. The parameter identification are established by hybrid learning using back-propagation algorithm and RLSE(Recursive Least Square Estimate). In the on-line design, the premise and consequent parameters are tuned to RLSE with forgetting factor due to a characteristic of time variant. Finally, we applied the proposed method to the water temperature control system and obtained better results than previous works such as fuzzy control.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.151-157
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• 2013

This paper proposes an online adaptive fuzzy controller for a wind energy conversion system (WECS) that is intrinsically highly nonlinear plant. In real application, to obtain exact system parameters such as power coefficient, many measuring instruments and off-line implementations are required, which is very difficult to perform. This shortcoming can be avoided by introducing fuzzy system in the controller design in this paper. The proposed adaptive fuzzy control scheme using self-structuring algorithm requires no system parameters to meet control objectives. Even the structure of the fuzzy system is automatically grows on-line, which distinguishes our proposed algorithm over the previously proposed fuzzy control schemes. Combining derivative estimator for wind velocity, the whole closed-loop system is shown to be stable in the sense of Lyapunov.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.2
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• pp.99-104
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• 2002

In this paper, we construct a hybrid intelligent controller based on a fusion scheme of GA(Genetic Algorithm) and FCM(Fuzzy C-Means) clustering-based ANFIS(Adaptive Neuro-Fuzzy Inference System). In the structure identification, a set of fuzzy rules are generated for a given criterion by FCM clustering algorithm. In the parameter identification, premise parameters are optimally searched by adaptive GA. On the other hand, consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. Finally, we applied the proposed method to the truck backer-upper control and obtained a better performance than previous works.