• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.928-938
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• 2008

This study presents an approach for approximation an unknown function from a numerical data set based on the synthesis of a neuro-fuzzy model. An adaptive input data space parting method, which is used for building hyperbox-shaped clusters in the input data space, is proposed. Each data cluster is implemented here as a fuzzy set using a membership function MF with a hyperbox core that is constructed from a min vertex and a max vertex. The focus of interest in proposed approach is to increase degree of fit between characteristics of the given numerical data set and the established fuzzy sets used to approximate it. A new cutting procedure, named NCP, is proposed. The NCP is an adaptive cutting procedure using a pure function $\Psi$ and a penalty function $\tau$ for direction the input data space parting process. New algorithms named CSHL, HLM1 and HLM2 are presented. The first new algorithm, CSHL, built based on the cutting procedure NCP, is used to create hyperbox-shaped data clusters. The second and the third algorithm are used to establish adaptive neuro- fuzzy inference systems. A series of numerical experiments are performed to assess the efficiency of the proposed approach.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.2
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• pp.125-131
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• 2017

2-Axis Pan and Tilt Motion Platform, a complex multivariate non-linear system, may incur any disturbance, thus requiring system controller with robustness against various disturbances. In this study, we designed an adaptive backstepping compensated controller by estimating the disturbance and error using the Radial Basis Function Neural Network (RBF NN). In this process, Uniformly Ultimately Bounded (UUB) was demonstrated via Lyapunov and stability was confirmed. By generating progressive disturbance to the irregular frequency and amplitude changes, it was verified for various environmental disturbances. In addition, by setting the RBF NN input vector to the minimum, the estimated disturbance compensation process was analyzed. Only two input vectors facilitated compensatory function of RBF NN via estimating the modeling and control error values as well as irregular disturbance; the application of the process resulted in improved backstepping controller performance that was confirmed through simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.361-364
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• 2013

Since the non-overlapping gate driver used in conventional DC-DC boost converters generates fixed dead-times, the converters suffer from the body-diode conduction loss or the charge-sharing loss. A adaptive control method has been proposed to reduce these loses. In this method, however, occurrence of and overlapping time of two power transistors in CCM results in reduction of efficiency. In this paper, to overcome this problem a new adaptive control method in proposed, and a DC-DC boost converter with the proposed adaptive control and power switching has been designed in a 0.35um CMOS process. The designed converter outputs 3.3V from a input voltage of 2.5V. The switching frequency is 500kHz and the maximum power efficiency is 95.3% at a load current 150mA. The designed chip area is $1720um{\times}1280um$.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.10
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• pp.748-754
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• 1987

A robustness analysis for model reference adaptive control(MRAC) system with plant uncertainty is discussed in this paper. The adaptive control system is designed under assumptions that the controlled plant is represented by a lst order nominal model and that the system is drived by a constant reference signal. When using general gradient method(GGM), it is shown that unmodelled dynamics in plant model can cause the instability of the overall control loop during the adaptation process. However, as the algorithm of least square method(LSM) is introduced, the global stability of the system can be hold. And it is also given that the boundedness of adjustable parameters may be verified using the concept of an equilibrium point analysis.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.71-71
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• 2000

In this paper, the fuzzy system is applied to MCZ Crystal Grower using at industrial field. The existing controller, which is PID controller, has a fixed gain and as a result of it it can not have an adaptive control function against the error or disturbance. Hence, the machine operator should always check the process status and when the error is occurred, the quality and the productivity may be decreased by each personal capability. In order to remove this drawback, a fuzzy control system which is known to be adaptive and flexible is applied to the machine. After applying the fuzzy system, and compared with the existing system, the diameter deviation and the defects were decreased. we proved the possibility of application fuzzy system to single silicon crystal grower.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.376-376
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• 2000

Several algorithmic models have been proposed to estimate software cost and other management parameters. In particular, early prediction of completion time is absolutely essential for proper advance planning and a version of the possible ruin of a project. However, estimation is difficult because of its similarity to export judgment approaches and for its potential as an expert assistant in support of human judgment. Especially, the nature of the Norden/Rayleigh curve used by Putnam, renders it unreliable during the initial phases of the project, in projects involving a fast manpower buildup, as is the case with most software projects. Estimating software development effort is more complexity, because of infrastructure software related to target-machines hardware and process characteristics should be considered in software development for DCS (Distributed Control System). In this paper, we propose software development effort estimation technique using adaptive neural fuzzy inference system. The methods is applied to case-based projects and discussed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.748-754
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• 1994

This paper deals with a geometric error simulator, measurement and inspection of workpiece errors on the machine tools, and identification and compensation methodology of thermal errors in machining centers. In order to raise the machining accuracy of workpieces a measurement and inspection system on the machine tool is developed. By using MPPGT module Manual and CNC type CMMs are realized on the machining centers. To compensate for geometric and thermal deformation errors of machining centers, a real time and an off line geometric adaptive control system were developed on the machining centers. A vertical and a horizontal machining center equipped with FANUC 0MC were used for experiments. Performance of the systems were confirmed with a large amount of experiment.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.690-692
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• 1998

In general, fuzzy control system are efficient for the systems which are complicated and nonlinear. But the fuzzy control flawed by the fact that it is much trial and errors in process of getting parameters of membership function which can express optimal status of system. This paper shows the methodology which is applied of ANFIS(Adaptive Neuro-Fuzzy Inference System) for the coverage against these defects. It proved superiority of ANFIS by controlling inverted pendulum.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.1
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• pp.33-39
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• 2011

H.264 decoders usually have pipeline architecture by a macroblock or a 4 ${\times}$ 4 sub-block. The period of the pipeline is usually fixed to guarantee the operation in the worst case which results in many idle cycles and higher data bandwidth. Adaptive pipeline architecture for H.264 decoders has been proposed for efficient decoding and lower the requirement of the bandwidth for the memory bus. However, it requires a controller for the adaptive priority control to utilize the advantage. We propose a smart bus arbiter that replaces the controller. It is introduced to adjust the priority adaptively the QoS (Quality of Service) control of the decoding process. The smart arbiter can be integrated the arbiter of bus systems and it works when certain conditions are met so that it does not affect the original functions of the arbiter. An H.264 decoder using the proposed architecture is designed and implemented to verify the operation using an FPGA.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.106-116
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• 1998

A position control algorithm of a flexible manipulator is studied. The proposed algorithm is based on an ACFAC(Automatic Constructed Fuzzy Adaptive Controller) system based on the neural network learning algorithms. The proposed system learns membership functions for input variables using unsupervised competitive learning algorithm and output information using supervised outstar learning algorithm. ACFAC does not need a dynamic modeling of the flexible manipulator. An ACFAC is designed that the end point of the flexible manipulator tracks the desired trajectory. The control input to the process is determined by error, velocity and variation of error. Simulation and experiment results show a robustness of ACFAC compared with the PID control and neural network algorithms.