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Abstract

In this paper several applications of genetic algorithm (GA) as an aid to the system identification process are presented. First, GAs are used in a set of covariance-based optimum input signal algorithms using a proposed architecture suitable for online system identification. The optimal signals are computed recursively using a predictive filter. The efficiency of these algorithms are compared based on a set of simulations. Second, a novel input design for a two-step identification scheme is presented. Constraint systems, such as commonly found in structural and biomedical engineering applications, are considered for the input design algorithm. This paper presents a novel approach that induces a learning scheme into the input design computation and allows for considerations of the given constraints of the system. The optimization of the new input signal is accomplished using a simple elitism based genetic algorithm. Simulation results indicate the proposed piecewise adaptive input design algorithm performs well compared to the general white-noise-based estimation results. In the third portion of this paper proof is given that no dynamic controller can reduce the noise influence in linear system identification. A new selection scheme of the corresponding singular values is proposed for the eigensystem realization portion of the Observer Kalman filter IDentification algorithm in noisy systems. The selection is done using a GA. Simulation results of the proposed algorithm in comparison with the traditional used method are presented. The results indicate an improved ability to extract system models from highly noise corrupted data.

Keywords

System identification genetic algorithms optimal input numerical simulations

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References

Aliprantis, D.C. , Sudhoff, S.D. , and Kuhn, B.T. , 2006, "Genetic algorithm-based parameter identification of a hysteretic brushless exciter model," IEEE Transactions on Energy Conversion 21(1), 148-154.

Broersen, P.M.T. and de Waele, S. , 2003, "Generating data with prescribed power spectral density ," IEEE Transactions on Instrumentation and Measurement 52(4), 1061-1067.

Chen, C.-W. , Huang, J.-K. , Phan, M. , and Juang, J.-N. , 1992, "Integrated system identification and state estimation for control of flexible space structures," Journal of Guidance, Control, and Dynamics 15(1), 88-95.

Chinvorarat, S. , Lu, B. , Huang, J.-K. , and Schoen, M.P. , 1999, "Setpoint tracking predictive control by system identification approach" in Proceedings of the American Control Conference, San Diego, CA, June 2-4, pp. 331-335.

Dasgupta, S. and Huang, Y.-F. , 1987, "Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor for systems with bounded noise", IEEE Transactions on Information Theory 33(3), 383-392.

Haupt, R.L. , and Haupt, S.E. , 1998, Practical Genetic Algorithms, Wiley , New York.

Juang, J.-N. , 1994, Applied System Identification, Prentice-Hall , Englewood Cliffs, NJ.

Juang, J.-N. , Cooper, J.E. , and Wright, J.R. , 1988, "An eigensystem realization algorithm using data correlation (ERA/DC) for modal parameter identification," Control-Theory and Advanced Technology 4(1), 5-14.

Juang, J.-N. , and Papa, R.S. , 1985, "An eigensystem realization algorithm for modal parameter identification and model reduction," Journal of Guidance, Control, and Dynamics 8(5), 620-627.

10.

Juang, J.-N. and Papa, R.S. , 1986, "Effects of noise on modal parameters identified by the eigensystem realization algorithm," Journal of Guidance, Control, and Dynamics 9(3), 294-303.

11.

Lee Y.H. , Marvin A.C. , and Porter S.J. , 1999, "Genetic algorithm using real parameters for array antenna design optimization," IEEE High Frequency Postgraduate Student Colloquium, 8-13.

12.

Levin, M.J. , 1960, "Optimum estimation of impulse response in the presence of noise," IRE Transaction on Circuit Theory CT- 7, 50-56.

13.

Mehra, R.K. , 1974, "Optimal input signals for parameter estimation in dynamic systems - survey and new results," IEEE Transaction on Automatic Control AC- 19, 753-768.

14.

Sano, A. , and Kikucki, M. , 1985, "Adaptive control of arterial oxygen pressure of newborn infants under incubator oxygen treatments," IEEE Proceedings 132, 205-211.

15.

Schoen, M.P. and Mahajan, A. , 1999, "Input design for the system identification (IDSI) of an ultrasonic 3D position estimation system," in Proceedings of the ASME Winter Meeting, Nashville, Tennessee, TN, November 14- 19, pp. 765-770.

16.

Schoen, M.P. , 2002, "Optimum input for system identification for systems with actuator saturation using genetic algorithms," in Proceedings of the IMECE 2002, New Orleans, LA, November 17-22.

17.

Thumati, B. and Schoen, M.P. , 2005, "Frequency based optimal input signal determination for ARX parameter estimation of constraint systems," in Proceedings of the IMECE 2005 Conference, Orlando, FL, November 5-11.

Application of Genetic Algorithms to Observer Kalman Filter Identification

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