• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.3
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• pp.487-492
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• 2002

In this paper, we proposed a control method of an unknown nonlinear system using a dynamical neural network. The proposed method is composed of neural network of state space model type, performs for a unknown nonlinear system, identification with using the dynamical neural network, and then a nonlinear adaptive controller is designed with these identified informations. In order to verify the effectiveness of the proposed method, we simulated one-link manipulator. The simulation results showed the effectiveness of using the dynamical neural network in the adaptive control of one-link manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.753-758
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• 1987

The purpose of ocean thermal energy conversion (OTEC) plant control is to provide stable power efficiently by appropriately regulating the seawater flow rates and the working fluid flow rate under conditions of continually changing seawater temperatures. This paper describes digital control of working fluid flow rate based on an adaptive control theory for the "Imari 2" OTEC plant at Saga University. Provisions have been made for linkage between the software of the adaptive control theory and the hardware of the OTEC plant. In implementing the working fluid flow rate control, if persistency of excitation conditions are lost, the algorithm of identification often exhibits bursting phenomena. To avoid this difficulty, the stopping-and-starting rule for identification was derived and was used for the working fluid flow rate control. Satisfactory control performance was then obtained by using this digital control system.ol system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.966-971
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• 2010

An adaptive state observer is presented for a class of LTI systems that have a sinusoidal disturbance in the measurement output. Adaptation rules are developed for identifying the unknown sinusoidal disturbance signal from the system output. For the application of the identification result to the state estimation problem, the sinusoidal signal with arbitrary initial phase has been considered in this paper. In order to test the performance of proposed algorithm, comparative computer simulations have been carried out with an existing robust observer. The simulation results show the effectiveness of the proposed method.

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.779-802
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• 2013

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.

• 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.

• ETRI Journal
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• v.4 no.3
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• pp.18-29
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• 1982

In a block adaptive filtering procedure, the filter coefficients are adjusted once per each output block while maintaining performance comparable to that of widely used LMS adaptive filtering in which the filter coefficients are adjusted once per each output data sample. An efficient implementation of block adaptive filter is possible by means of discrete transform technique which has cyclic convolution property and fast algorithms. In this paper, the block adaptive filtering using Fermat Number Transform (FNT) is investigated to exploit the computational efficiency and less quantization effect on the performance compared with finite precision FFT realization. And this has been verified by computer simulation for several applications including adaptive channel equalizer and system identification.

• Journal of Ocean Engineering and Technology
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• v.8 no.2
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• pp.131-140
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• 1994

Dynamics of AUV has heavy nonlinearities and many unknown parameters due to its bluff shape and low cruising speed. Intelligent algorithms, therefore, are required to overcome these nonlinearities and unknown system dynamics. Several identification techniques have been suggested for the application of control of underwater vehicles during last decade. This paper applies the neural network to identification and motion control problem of AUVs. Nonlinear dynamic systems of an AUV are identified using feedforward neural network. Simulation results show that the learned neural network can generate the motion of AUV. This paper, also, suggest an adaptive control scheme up-dates the controller weights with reference model and feedforward neural network using error back propagation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.233-242
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• 1995

In this paper, we introduce the identification model of dynamic system using the neural networks, We propose two identification models. The output of the parallel identification model is a linear combination of its past values as well as those of the input. The series-parallel model is a linear combination of the past values in the input and output of the plant. To generate stable adaptive laws, we prove that the series-parallel model is found to be proferable.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.540-545
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• 1988

In this study a microcomputer-aided design program has been developed to design and analysis for the digital adaptive control system. DACS(Digital Adaptive Control System) program has been written in GWBASIC language which is suitable for IBM-PC compatible. The dynamics of each element was modulized and described by linear difference equations. By the aid of this program, sampling time, the number of bits of A/D and D/A converter and the stability for the digital adaptive control system can be determined. In order to estimate the system parameters an on-line identification and a regression analysis method are utilized. The simulation results have been well agreed with the experiments. To demonstrate the utility of this program, an adaptive control system has been designed for air-heating system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1993.04a
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• pp.140-146
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• 1993

Active noise control uses the intentional superposition of acoustic waves to create a destructive interference pattern such that a reduction of the unwanted sound occurs. In active noise control system the choice of a control structure and design of the controller are the main issues of concern. In real acoustic fields there are a vast number of noise sources with time-varying nature and the characteristics of transducers and the geometric set-up of control system are subject to change. Accordingly the control system should be designed to adapt such circumstances so that required level of performance is maintained. In this paper, the adaptive control algorithm for self-tuning adaptive controller is presented for the application in active noise control system. Self-tuning is a direct integration of identification and controller design algorithm in such a manner that the two processes proceed sequentially. The least mean square algorithm was used for the identification schemes and adaptive weighted minimum variance control algorithm was applied for self-tuning controller. Computer simulation results for self-tuning feedback controller are presented. And simulation results was shown to be useful for the situation in which the periodic noise sources act on the acoustic field.