• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.14-19
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• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.27-30
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• 1996

PR interval variability has been proposed as a noninvasive tool for in-vestigating the autonomic nervous system as welt as heart rate variability. The goal of this paper is to determine whether PR interval variability is generated from deterministic nonlinear dynamics. The data used in this study is a 24-hour bolter ECGs of 20 healthy adults. We developed an automatic PR interval measurement algorithm, and tested it using MIT ECG Databases. The general discriminants of nonlinear dynamics, such as, correlation dimension and phase space reconstruction are used. Surrogate data is generated from simpler linear models to have similar statistical characteristics with the original data. Nonlinear discriminants are applied to both data, and compared for any significant results. It was concluded that PR interval variability shows non-linear characteristics.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1312-1315
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• 1990

Nanometer positioning control with high velocity and long stroke is discussed. A one-axis stage mechanism driven by an AC linear motor and guided by a rolling ball guide has been constructed. Coarse and fine position controls are designed by using nonlinear dynamics of the rolling guide. Switching from coarse positioning to fine positioning is studied.

• Earthquakes and Structures
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• v.8 no.4
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• pp.915-934
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• 2015

Within the context of Structural Health Monitoring (SHM), it is often the case that structural systems are described by uncertainty, both with respect to their parameters and the characteristics of the input loads. For the purposes of system identification, efficient modeling procedures are of the essence for a fast and reliable computation of structural response while taking these uncertainties into account. In this work, a reduced order metamodeling framework is introduced for the challenging case of nonlinear structural systems subjected to earthquake excitation. The introduced metamodeling method is based on Nonlinear AutoRegressive models with eXogenous input (NARX), able to describe nonlinear dynamics, which are moreover characterized by random parameters utilized for the description of the uncertainty propagation. These random parameters, which include characteristics of the input excitation, are expanded onto a suitably defined finite-dimensional Polynomial Chaos (PC) basis and thus the resulting representation is fully described through a small number of deterministic coefficients of projection. The effectiveness of the proposed PC-NARX method is illustrated through its implementation on the metamodeling of a five-storey shear frame model paradigm for response in the region of plasticity, i.e., outside the commonly addressed linear elastic region. The added contribution of the introduced scheme is the ability of the proposed methodology to incorporate uncertainty into the simulation. The results demonstrate the efficiency of the proposed methodology for accurate prediction and simulation of the numerical model dynamics with a vast reduction of the required computational toll.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.104-112
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• 2001

Simply, Nonlinear dynamics theory means the complicated and noise-like phenomena originated form nonlinearity involved in deterministic dynamical system. An almost all the natural signals have nonlinear property. However, there exist few analysis software tool or package for a research and development of applications. We develop nonlinear time series analysis simulator is to provide a common and useful tool for this purpose and to promote research and development of nonlinear dynamics theory. This simulator is consists of the following four modules such as generation module, preprocessing module, analysis module and ICA module. In this paper, we applied to Electroencephalograph (EEG), as it turned out, our simulator is able to analyze nonlinear time series. Besides, we could get the useful results using the various parameters. These results are used to diagnostic the brain diseases.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.18-21
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• 1996

Even though there are several deterministic methods for the modeling of linear systems, there is no standard method for the modeling of nonlinear systems. For the modeling of nonlinear systems we have applied the genetic programming method to estimate nonlinear time sereis. We get the time series from the simple known nonlinear dynamics, and fed those to genetic programming. For the tested nonlinear systems, suggested method estimated the nonlinear dynamics correctly.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.139-142
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• 1997

In this paper, a robust controller is proposed to achieve an accurate tracking for an uncertain nonlinear plant with actuator dynamics. The extent of parameter uncertainty can be quantified by using linear parameterization technique. A switching controller is proposed to guarantee the global asymptotic stability of the plant. In order to eliminate the chattering caused by the switching controller, a smoothing controller is designed using the boundary layer technique around the sliding surface and guarantees the uniform ultimate boundedness of the tracking error.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.255-270
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• 2006

In this paper, a design method of nonlinear autopilot for ship-to-ship missiles is proposed. Ship-to-ship missiles have strongly coupled dynamics through roll, yaw, and pitch channel in comparison with general STT type missiles. Thus it becomes difficult to employ previous control design method directly since we should find three different solutions for each control fin deflection and should verify the stability for more complicated dynamics. In this study, we first propose a control loop structure for roll, yaw, and pitch autopilot which can determine the required angles of all three control fins. For yaw and pitch autopilot design, missile model is reduced to a minimum phase model by applying a singular perturbation like technique to the yaw and pitch dynamics. Based on this model, a multi-input multi-output (MIMO) nonlinear autopilot is designed. And the stability is analyzed considering roll influences on dynamic couplings of yaw and pitch channel as well as the aerodynamic couplings. Some additional issues on the autopilot implementation for these coupled missile dynamics are discussed. Lastly, 6-DOF (degree of freedom) numerical simulation results are presented to verify the proposed method.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.323-326
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• 1997

In this study, we investigated the circardian rhythm of complexity of cardiac dynamics in humans. Dynamic 24-hour electrocardiographic recordings were obtained from 30 healthy ambulant subjects aged 41 to 50 years. or each recordings, normalized low frequency (0.04-0.1 hertz) and high frequency (>0.15 hertz) component are calculated. our different indexes obtained from separate algorithms of nonlinear dynamics - approximate entropy, correlation dimension, Lyapunov exponent and fractal dimension - were calculated. During early morning, low frequency component rose rapidly with concomitant withdrawl of high frequency component. All the our indexes of nonlinear dynamics showed remarkably same circardian rhythm: an early morning dip preceded by a steep decline during late night, a gradual recovery during evening and a peak around midnight. These data indicate that the simultansous losses of all of the our different mechanisms of nonlinear control of heart rate during early morning, concomitent with the surge of symapathetic activity and reduction of vagal activity, may contribute to the increased incidence of cardiovascular events during morning hours.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.993-1000
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• 2001

Configuration-dependent nonlinear coefficient matrices in the dynamic equation of robot manipulator impose computa- tional burden in real-time implementation of tracking control based on the inverse dynamics controller. However, parallel manipulators such as Stewart platform have relatively small workspace compared to serial manipulators. Based on the characteristics of small motion range. nonlinear coefficient matrices can be approxiamted to constant ones. The modeling errors caused by such approximation are compensated for by H-infinity controller that treats the modeling errors disturbance. The proposed inverse dynamics controller with approximate dynamics combined with H-infinity control shows good tracking performance even for fast tracking control in which computation of full inverse dynamics is not easy to implement.