Nonlinear model identification of an experimental ball-and-tube system using a genetic programming approach

Mechanical Systems and Signal Processing

Volumn 23, Issue 5, 2009, Pages 1434-1446

  Coelho, Leandro dos Santos ^a   Pessôa, Marcelo Wicthoff ^a  

^a PONTIFICAL CATHOLIC UNIVERSITY OF PARANÁ   (Brazil)

Author keywords

Evolutionary algorithm; Genetic programming; Nonlinear models; System identification

Indexed keywords

APPROXIMATION THEORY; COMPUTER SIMULATION; COMPUTER SYSTEMS PROGRAMMING; EVOLUTIONARY ALGORITHMS; FAULT DETECTION; GENETIC ALGORITHMS; GENETIC PROGRAMMING; MATHEMATICAL MODELS; MODELS; NONLINEAR SYSTEMS; TREES (MATHEMATICS);

APPROXIMATE MODELS; COMPUTER PROGRAMS; CROSSOVER AND MUTATIONS; DESCRIPTION METHODS; DISCRETE POLYNOMIALS; EMPIRICAL CASE STUDIES; EVOLUTIONARY COMPUTATIONS; IDENTIFICATION PROBLEMS; LEAST-SQUARES; MODEL - BASED CONTROLS; NARX MODELING; NATURAL EVOLUTIONS; NON-LINEAR AUTOREGRESSIVE WITH EXOGENOUS; NONLINEAR MODEL IDENTIFICATIONS; NONLINEAR MODELS; NONLINEAR SYSTEM IDENTIFICATION PROCEDURES; OPTIMIZATION PROBLEMS; PROCESS DYNAMICS; STRUCTURE SELECTIONS; SYSTEM IDENTIFICATION; SYSTEM IDENTIFICATION TECHNIQUES; TIME-VARYING; TRADITIONAL APPROXIMATIONS; TREE STRUCTURES; TUBE SYSTEMS; UNDERLYING SYSTEMS;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 63449119001     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2009.02.005     Document Type: Article

References (52)

1. Giannakis G.B., and Serpedin E. A bibliography on nonlinear system identification. Signal Processing 81 3 (2001) 533-580
2. Billings S.A. Identification of nonlinear systems-a survey. IEE Proceedings 127 6 (1980) 272-285
3. L. Ljung, Black-box models from input-output measurements, in: Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference, Budapest, Hungary, 2001, pp. 138-146.
4. Haber R., and Unbehauen H. Structure identification of nonlinear dynamic systems-a survey on input/output approaches. Automatica 26 4 (1990) 651-677
5. Aguirre L.A., Coelho M.C.S., and Corrêa M.V. On the interpretation and practice of dynamical differences between Hammerstein and Wiener models. IEE Proceedings, Part D: Control Theory and Applications 152 4 (2004) 349-356
6. Kim K.I., and Powers E.J. A digital method of modelling quadratically nonlinear systems with a general random input. IEEE Transactions on Acoustics, Speech, and Signal Processing 36 11 (1988) 1758-1769
7. Chen S., and Billings S.A. Representations of nonlinear systems: the NARMAX model. International Journal of Control 49 3 (1989) 1013-1032
8. Wong C.X., and Worden K. Generalised NARX shunting neural network modelling of friction. Mechanical Systems and Signal Processing 21 1 (2007) 553-572
9. Pawar P.M., and Ganguli R. Genetic fuzzy system for online structural health monitoring of composite helicopter rotor blades. Mechanical Systems and Signal Processing 21 5 (2007) 2212-2236
10. Lei Y., He Z., and Hu Q. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mechanical Systems and Signal Processing 21 5 (2007) 2280-2294
11. Tan M.H., and Hammond J.K. A non-parametric approach for linear system identification using principal component analysis. Mechanical Systems and Signal Processing 21 4 (2007) 1576-1600
12. Chen Q., Worden K., Peng P., and Leung A.Y.T. Genetic algorithm with an improved fitness function for (N)ARX modeling. Mechanical Systems and Signal Processing 21 2 (2007) 994-1007
13. Chang W.D. An improved real-coded genetic algorithm for parameters estimation of nonlinear systems. Mechanical Systems and Signal Processing 20 1 (2006) 236-246
14. Lew T.L., Spencer A.B., Scarpa F., Worden K., Rutherford A., and Hemez F. Identification of response surface models using genetic programming. Mechanical Systems and Signal Processing 20 8 (2006) 1819-1831
15. Gray G.J., Li Y., Murray-Smith D.J., and Sharman K.C. Structural system identification using genetic programming and a block diagram oriented simulation tool. Electronic Letters 32 15 (1996) 1423-1424
16. Dracopoulos D.C., and Kent S. Genetic programming for prediction and control. Neural Computing & Applications 6 4 (1995) 214-228
17. Rodríguez-Vásquez K., and Fleming P.J. Multi-objective genetic programming for nonlinear system identification. Electronics Letters 34 9 (1998) 930-931
18. Beliagiannis G.N., Skarlas L.V., Likothanassis S.D., and Perdikouri K.G. Nonlinear model structure identification of complex biomedical data using a genetic-programming-based technique. IEEE Transactions on Instrumentation and Measurement 54 6 (2005) 2184-2190
19. Yang W.-X. Establishment of the mathematical model for diagnosing the engine valve faults by genetic programming. Journal of Sound and Vibration 293 1-2 (2006) 213-226
20. Zhang L., and Nandi A.K. Fault classification using genetic programming. Mechanical Systems and Signal Processing 20 3 (2007) 1273-1284
21. Zhang L., Jack L.B., and Nandi A.K. Fault detection using genetic programming. Mechanical Systems and Signal Processing 19 2 (2005) 271-289
22. Chen P., Taniguchi M., Toyota T., and He Z. Fault diagnosis method for machinery in unsteady operating condition by instantaneous power spectrum and genetic programming. Mechanical Systems and Signal Processing 19 1 (2005) 175-194
23. Koza J.R. Genetic Programming: On the Programming of Computers by Means of Natural Selection (1992), MIT Press, Cambridge, MA, USA
24. Korenberg M., Billings S.A., Liu Y.P., and McIlroy P.J. Orthogonal parameter estimation algorithm for non-linear stochastic systems. International Journal of Control 48 1 (1998) 193-210
25. Assunção R., and Coelho L.S. B-spline neural network design using artificial immune network applied to identification of a ball-and-tube prototype. Soft Computing in Industrial Applications. Advances in Soft Computing 39 (2007) 92-101
26. Mádar J., Abonyi J., and Szeifert F. Genetic programming for the identification of nonlinear input-output models. Industrial and Engineering Chemistry Research 44 (2005) 3178-3186
27. Ljung L. System Identification-Theory for the User. second ed. (1999), PTR Prentice-Hall, Upper Saddle River, USA
28. Schoukens J., and Pintelon R. Identification of Linear Systems: A Practical Guideline to Accurate Modeling (1991), Pergamon Press, New York, NY, USA
29. Kerschen G., Worden K., Vakakis A.F., and Golinval J.C. Past, present and future of nonlinear system identification in structural dynamics. Mechanical Systems and Signal Processing 20 3 (2006) 505-592
30. Sjöberg J., Zhang Q., Ljung L., Beneviste A., Delyon B., Glorennec P.-Y., Hjalmarsson H., and Juditsky A. Nonlinear black-box modelling in system identification: a unified overview. Automatica 31 12 (1995) 1691-1724
31. Abdelazim T., and Malik O.P. Identification of nonlinear systems by Takagi-Sugeno fuzzy logic grey box modeling for real-time control. Control Engineering Practice 13 12 (2005) 1489-1498
32. Söderström T., and Stoica P. System Identification, International Series in Systems & Control (1994), PTR Prentice-Hall, Upper Saddle River, USA
33. Tsakonas A. A comparison of classification accuracy of for genetic programming-evolved intelligent structures. Information Sciences 176 6 (2006) 691-724
34. Eiben A.E., Hinterding R., and Michalewicz Z. Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 3 2 (1999) 124-141
35. May R. Simple mathematical models with very complicated dynamics. Nature 261 (1976) 459-467
36. Coelho L.S., and Mariani V.C. Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect. IEEE Transactions on Power Systems 21 2 (2006) 989-996
37. Nicolao G.D., and Trecate G.F. Consistent identification of NARX models via regularization networks. IEEE Transactions on Automatic Control 44 11 (1999) 2045-2049
38. J. Mádar, J. Abonyi, F. Szeifert, Genetic programming for system identification, in: Proceedings of the Fourth IEEE International Conference on Intelligent Systems Design and Applications (ISDA 2004) Conference, Budapest, Hungary, 2004.
39. Landau I.D. Identification in closed loop: a powerful design tool (better design models, simpler controllers). Control Engineering Practice 9 1 (2001) 51-65
40. Lakshminarayanan S., Emoto G., Ebara S., Tomida K., and Shah S.L. Closed loop identification and control loop reconfiguration: an industrial case study. Journal of Process Control 11 (2001) 587-599
41. U. Forssell, Closed-loop identification methods, theory, and applications, Ph.D. Dissertation, Department of Electrical Engineering, Linköping University, Linköping, Sweden, 1999.
42. Huang B. On-line closed loop model validation and detection of abrupt parameter changes. Journal of Process Control 11 6 (2001) 699-715
43. Huang B., and Shah S. Closed-loop identification: a two step approach. Journal of Process Control 7 6 (1997) 425-438
44. Gilsona M., and Van den Hof P.M.J. Instrumental variable methods for closed-loop system identification. Automatica 41 2 (2005) 241-249
45. Van den Hof P.M.J. Closed-loop issues in system identification. Annual Reviews in Control 22 (1998) 173-186
46. Van den Hof P.M.J., and Schrama R.J.P. Identification and control-closed loop issues. Automatica 31 12 (1995) 1751-1770
47. Forssell U., and Ljung L. Closed-loop identification revisited. Automatica 35 7 (1999) 1215-1241
48. Zhou Y., Han A., Yan S., and Chen X. A fast method for online closed-loop system identification. International Journal of Advanced Manufacturing Technology 31 1 (2006) 78-84
49. Yuwana M., and Seborg D.E. A new method for on-line controller tuning. AIChE Journal 28 3 (1982) 434-440
50. Schaible B., Xie H., and Lee Y.C. Fuzzy logic models for ranking process effects. IEEE Transactions on Fuzzy Systems 5 4 (1997) 545-556
51. Billings S.A., and Zhu Q.M. Nonlinear model validation using correlation tests. International Journal of Control 60 6 (1994) 1107-1120
52. Billings S.A., and Voon W.S.F. Correlation based model validity tests for non-linear models. International Journal of Control 44 1 (1996) 233-244