• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.301-305
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• 1996

$\mu$ theory can handle the parametric uncertainty and produces more non-conservative controller than H$_{\infty}$ control theory. However an existing solution of the theory, D-K iteration, creates a controller of huge order and cannot handle the real or mixed real-complex perturbation sets. In this paper, we use genetic algorithms to solve these problems of the D-K iteration method. The Youla parameterization is used to obtain all stabilizing controllers and the genetic algorithms determines the values of the state feedback gain, the observer gain, and Q parameter to minimize $\mu$, the structured singular value, of given system. From an example, we show that this method produces lower order controller which controls a real parameter-perturbed plant than D-K iteration method.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.753-759
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• 2011

The HIV (Human Immunodeficiency Virus) causes AIDS (Acquired Immune Deficiency Syndrome). The process of infection and mutation by HIV can be described by a 3rd order state equation. For this HIV model that includes the dynamics of the mutant virus, we present a parameter estimation scheme using two state variables sporadically measured, out of the three, by employing a genetic algorithm. It is assumed that these non-uniformly sampled measurements are subject to random noises. The effectiveness of the proposed parameter estimation is demonstrated by simulations. In addition, the estimated parameters are used to analyze the equilibrium points of the HIV model, and the results are shown to be consistent with those previously obtained.

• Journal of Korea Water Resources Association
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• v.33 no.S1
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• pp.3-10
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• 2000

A newly developed heuristic algorithm, Harmony Search, is applied to the parameter calibration problem of the nonlinear Muskingum model. The Harmony Search could, mimicking the improvisation of music player, find better parameter values for in the nonlinear Muskingum model than five other methods including another heuristic method, genetic algorithm, in the aspect of SSQ(the sum of the square of the deviations between the observed and routed outflows) as well as in the aspects of SAD(the sum of the absolute value of the deviations), DPO(deviations of peak of routed and actual flows) and DPOT(deviatios of peak time of routed and actual outflow). Harmony Search also has the advantage that it does not require the process of asuming the initial values of desing parameters. The sensitivity analysis of Harmony Memory Considering Rate showed that relatively large values of Harmony Memory Considering Rate makes the Harmony Search converge to a better solution.

• Proceedings of the Korea Water Resources Association Conference
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• 2000.05a
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• pp.3-10
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• 2000

A newly developed heuristic algorithm, Harmony Search, is applied to the parameter calibration problem of the nonlinear Muskingum model. The Harmony Search could, mimicking the improvisation of music players, find better parameter values for in the nonlinear Muskingum model than five other methods including another heuristic method, genetic algorithm, in the aspect of SSQ (the sum of the square of the deviations between the observed and routed outflows) as well as in the aspects of SAD (the sum of the absolute value of the deviations), DPO (deviations of peak of routed and actual flows) and DPOT (deviations of peak time of rented and actual outflow). Harmony Search also has the advantage that it does not require the process of assuming the initial values of design parameters. The sensitivity analysis of Harmony Memory Considering Rate showed that relatively large values of Harmony Memory Considering Rate makes the Harmony Search converse to a better solution.

• Structural Engineering and Mechanics
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• v.70 no.2
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• pp.209-219
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• 2019

By normalizing the internal hysteresis variable and eliminating the redundant parameter, the normalized Bouc-Wen model is considered to be an improved and more reasonable form of the Bouc-Wen model. In order to facilitate application and further research of the normalized Bouc-Wen model, some key aspects of the model need to be uncovered. In this paper, hysteresis characterization of the normalized Bouc-Wen model is first studied with respect to the model parameters, which reveals the influence of each model parameter to the shape of the hysteresis loops. The parameter identification scheme is then proposed based on an improved genetic algorithm (IGA), and verified by experimental test data. It is proved that the proposed method can be an efficacious tool for identification of the model parameters by matching the reconstructed hysteresis loops with the target hysteresis loops. Meanwhile, the IGA is shown to outperform the standard GA. Finally, a simplified identification method is proposed based on parameter sensitivity, which indicates that the efficiency of the identification process can be greatly enhanced while maintaining comparable accuracy if the low-sensitivity parameters are reasonably restricted to narrower ranges.

• Proceedings of the KIEE Conference
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• 2002.11d
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• pp.313-316
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• 2002

In this paper, we present an online scheme for parameter estimation of solar cell, based on the model adjustment technique and a genetic algorithm. The ideal diode model and the diode model with series and shunt resistor are used to estimate their parameters. Simulation works using field data in the form of a VI characteristic curve are carried out to demonstrate the effectiveness of the proposed method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.1311-1312
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• 2011

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.448-448
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• 2018

In this study, the regression models (Load ESTimator and eight-parameter model) were evaluated to estimate instantaneous pollutant loads under various criteria and optimization methods. As shown in the results, LOADEST commonly used in interpolating pollutant loads could not necessarily provide the best results with the automatic selected regression model. It is inferred that the various regression models in LOADEST need to be considered to find the best solution based on the characteristics of watersheds applied. The recently developed eight-parameter model integrated with Genetic Algorithm (GA) and Gradient Descent Method (GDM) were also compared with LOADEST indicating that the eight-parameter model performed better than LOADEST, but it showed different behaviors in calibration and validation. The eight-parameter model with GDM could reproduce the nitrogen loads properly outside of calibration period (validation). Furthermore, the accuracy and precision of model estimations were evaluated using various criteria (e.g., $R^2$ and gradient and constant of linear regression line). The results showed higher precisions with the $R^2$ values closed to 1.0 in LOADEST and better accuracy with the constants (in linear regression line) closed to 0.0 in the eight-parameter model with GDM. In hence, based on these finding we recommend that users need to evaluate the regression models under various criteria and calibration methods to provide the more accurate and precise results for pollutant load estimations.

• Structural Engineering and Mechanics
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• v.71 no.1
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• pp.45-56
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• 2019

A modified mechanical model of pre-pressed spring self-centering energy dissipation (PS-SCED) brace is proposed, and the hysteresis band is distinguished by the indication of relevant state variables. The MDOF frame system equipped with the braces is formulated in an incremental form of linear acceleration method. A multi-objective genetic algorithm (GA) based brace parameter optimization method is developed to obtain an optimal solution from the primary design scheme. Parameter sensitivities derived by the direct differentiation method are used to modify the change rate of parameters in the GA operator. A case study is conducted on a steel braced frame to illustrate the effect of brace parameters on node displacements, and validate the feasibility of the modified mechanical model. The optimization results and computational process information are compared among three cases of different strategies of parameter change as well. The accuracy is also verified by the calculation results of finite element model. This work can help the applications of PS-SCED brace optimization related to parameter sensitivity, and fulfill the systematic design procedure of PS-SCED brace-structure system with completed and prospective consequences.

• Journal of KSNVE
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• v.11 no.1
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• pp.147-155
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• 2001

This paper is concerned with the real-time automatic tuning of the multi-input multi-output positive position feedback controllers for smart structures by the genetic algorithms. The genetic algorithms have proven its effectiveness in searching optimal design parameters without falling into local minimums thus rendering globally optimal solutions. The previous real-time algorithm that tunes a single control parameter is extended to tune more parameters of the MIMO PPF controller. We employ the MIMO PPF controller since it can enhance the damping value of a target mode without affecting other modes if tuned properly. Hence, the traditional positive position feedback controller can be used in adaptive fashion in real time. The final form of the MIMO PPF controller results in the centralized control, thus it involves many parameters. The bounds of the control Parameters are estimated from the theoretical model to guarantee the stability. As in the previous research, the digital MIMO PPF control law is downloaded to the DSP chip and a main program, which runs genetic algorithms in real time, updates the parameters of the controller in real time. The experimental frequency response results show that the MIMO PPF controller tuned by GA gives better performance than the theoretically designed PPF. The time response also shows that the GA tuned MIMO PPF controller can suppress vibrations very well.