• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.782-787
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• 2006

The role of Unmanned Aircraft Vehicles(UAVs) has been increasing significantly in both military and civilian operations. Many complex systems, such as UAVs, are difficult to model accurately because they exhibit nonlinearity and show variations with time. Therefore, the control system must address the issues of uncertainty, nonlinearity, and complexity. Hence, identification of the mathematical model is an important process in controller design. In this paper, attitude dynamics identification of UAV is investigated. Using the flight data, nonlinear state space model for attitude dynamics of UAV is derived and verified. Real time simulation results show that the model dynamics match experimental data.

• Smart Structures and Systems
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• v.20 no.5
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• pp.619-636
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• 2017

Magnetorheological (MR) damper is a type of controllable device widely used in vibration mitigation. This device is highly nonlinear, and exhibits strongly hysteretic behavior that is dependent on both the motion imposed on the device and the strength of the surrounding electromagnetic field. An accurate model for understanding and predicting the nonlinear damping force of the MR damper is crucial for its control applications. The MR damper models are often identified off-line by conducting regression analysis using data collected under constant voltage. In this study, a MR damper model is integrated with a model for the power supply unit (PSU) to consider the dynamic behavior of the PSU, and then a real-time nonlinear model updating technique is proposed to accurately identify this integrated MR damper model with the efficiency that cannot be offered by off-line methods. The unscented Kalman filter is implemented as the updating algorithm on a cyber-physical model updating platform. Using this platform, the experimental study is conducted to identify MR damper models in real-time, under in-service conditions with time-varying current levels. For comparison purposes, both off-line and real-time updating methods are applied in the experimental study. The results demonstrate that all the updated models can provide good identification accuracy, but the error comparison shows the real-time updated models yield smaller relative errors than the off-line updated model. In addition, the real-time state estimates obtained during the model updating can be used as feedback for potential nonlinear control design for MR dampers.

• 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.

• Earthquakes and Structures
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• v.18 no.4
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• pp.493-506
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• 2020

In this paper, a new opposition based charged system search (CSS) is proposed to be used as a parameter identification of highly nonlinear semi-active magneto-rheological damper. By replacing the opposition particles with current solutions, the mentioned strategy is used to enhance the search space and to increase the exploration of CSS. To investigate the effectiveness of the proposed method, a nonlinear modified Bouc-Wen model of MR damper is considered to find its parameters, and compare it with those achieved from experimental model of MR damper. Also, by exploiting the sensitivity analysis and using the importance vector, the less importance parameters in the Bouc-Wen model are eliminated which makes the MR damper model simpler. Results demonstrate the new proposed algorithm (OBLCSS) has a high ability to tackle highly nonlinear problems. Based on the results of the α importance vector, a simplified model is proposed and its parameters are identified by using the presented OBLCSS algorithm. The simplified proposed model also has a high capability of estimating damper responses.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.4
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• pp.895-905
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• 1999

This paper identifies a linear time-invariant mathematical model of each stand of a five-stand tandem cold mill to design a robust $H_\infty$ thickness controller by applying input and output data sets to N4SID (Numerical algorithms for Subspace State Space System Identification) method. The input-output data sets describe interstand interference in the process of tandem cold rolling and are obtained from a nonlinear simulator of the tandem cold mill. In result, it is shown that the identified model well approximates the nonlinear model than a Taylor linearized model. Furthermore, uncertainties including roll eccentricity and incoming strip variation are quantitatively analyzed from the plot of maximum singular values.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.485-485
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• 2000

A design method of model following control system using neural networks is proposed. An unknown nonlinear single-input single-output plant is identified using a multilayer neural networks. A linear controller is designed fer the linear approximation model obtained by linearinzing the identification model. The identification model is also used as a plant emulator to obtain the prediction error. Deficient servo performance due to controlling nonlinear plant with only linear controller is mended by adjusting the linear controller output using the prediction output and the parameters of the identification model. An optimal preview controller is adopted as the linear controller by reason of having good servo performance lowering the peak of control input. Validity of proposed method is illustrated through a numerical simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.857-860
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• 1993

In this paper, we apply the self generating neuro fuzzy model (SGNFM) to the dimension analysis of the chaotic time series. Firstly, we formulate a nonlinear time series identification problem with nonlinear autoregressive (NARMAX) model. Secondly, we propose an identification algorithm using SGNFM. We apply this method to the estimation of embedding dimension for chaotic time series, since the embedding dimension plays an essential role for the identification and the prediction of chaotic time series. In this estimation method, identification problems with gradually increasing embedding dimension are solved, and the identified result is used for computing correlation coefficients between the predicted time series and the observed one. We apply this method to the dimension estimation of a chaotic pulsation in a finger's capillary vessels.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.2
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• pp.458-466
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• 1998

In this work, a frequency domain method is tested numerically and experimentally to improve nonlinear model parameters using the frequency response function at the nonlinear element connected point of structure. This method extends the force-state mapping technique, which fits the nonlinear element forces with time domain response data, into frequency domain manipulations. The force-state mapping method in the time domain has limitations when applying to complex real structures because it needd a time domain lumped parameter model. On the other hand, the frequency domain method is relatively easily applicable to a complex real structure having nonlinear elements since it uses the frequency response function of each substurcture. Since this mehtod is performed in frequency domain, the number of equations required to identify the unknown parameters can be easily increased as many as it needed, just by not only varying excitation amplitude bot also selecting excitation frequency domain method has some advantages over the classical force-state mapping technique in the number of data points needed in curve fit and the sensitivity to response noise.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.166-169
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• 2004

In this paper, computational analysis and experiments of subwoofer for car audio speaker system were performed and discussed to analyze acoustical phenomena for subwoofer. Ported enclosure system with subwoofer were manufactured and provided for test and simulation purposes. Subwoofer with single voice coil and double voice coil were identified by linear and nonlinear parameter identification method for loudspeaker parameters. For high power inputs to subwoofer, sound pressure levels were compared according to input powers with linear and nonlinear loudspeaker models. For subwoofer system with high power nonlinear speaker model was showed to be adequate to describe the behaviour of loudspeaker.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.26.2-26
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• 2001

It is known that Volterra kernel model can represent a wide variety of nonlinear chemical processes. But almost all Volterra kernel models which appeared in the literature are up to second order, because it was difficult to measure higher order Volterra kernels. Kashiwagi has recently shown a method for measuring Volterra kernels up to third order using pseudorandom M-sequence signals. In this paper, the authors verified the applicability of this method for chemical processes using polymerization reactor simulation. Also, the authors have recently proposed a practical Identification method for chemical processes, which is based on the combination of off-line nonlinear identification and on-line linear identification. This method is also applied to the identification of polymerization reactor, and we obtained ...