Nonlinear system identification: Comparison between PRBS and Random Gaussian perturbation on steam distillation pilot plant

This paper is proposed to model the steam temperature on steam distillation pilot plant using system identification. Random Gaussian Signal (RGS) and Pseudo Random Binary Sequence (PRBS) have been implemented to this system to perturb the input of the process. The objective of using different perturbation signal is to study their capability to excite the nonlinearity behavior of system dynamic. The linear and nonlinear Auto Regressive with Exogenous Input (ARX) model structures is used to estimate and validate the temperature output model of steam distillation pilot plant. Both models will be compared to study the performance and flexibility. The validation test is performed by using auto-correlation function (ACF), cross-correlation function (CCF) and model fit.

[1]  Ramli Adnan,et al.  Analysis of weight decay regularisation in NNARX nonlinear identification , 2009, 2009 5th International Colloquium on Signal Processing & Its Applications.

[2]  Figuroa Hernan Guidi.  Open and closed loop model identification and validation , 2009 .

[3]  A. Soni.  Control-Relevant System Identification using Nonlinear Volterra and Volterra-Laguerre Models , 2006 .

[4]  Stephane Dudret,et al.  Stability and asymptotic observers of binary distillation processes described by nonlinear convection/diffusion models , 2012, 2012 American Control Conference (ACC).

[5]  Daniel E. Rivera,et al.  Multi-level pseudo-random signal design and "model-on-demand" estimation applied to nonlinear identification of a RTP wafer reactor , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[6]  I. M. Yassin,et al.  Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network , 2012, 2012 IEEE 8th International Colloquium on Signal Processing and its Applications.

[7]  Leandro dos Santos Coelho,et al.  Nonlinear System Identification Based on B-Spline Neural Network and Modified Particle Swarm Optimization , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[8]  Jan Swevers,et al.  Identification of nonlinear systems using Polynomial Nonlinear State Space models , 2010, Autom..

[9]  O. Nelles.  Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models , 2000 .

[10]  Keith R. Godfrey,et al.  Perturbation signals for system identification , 1993 .

[11]  T. G. Ling,et al.  System identification of electro-hydraulic actuator servo system , 2011, 2011 4th International Conference on Mechatronics (ICOM).

[12]  Hyunjin Lee,et al.  High-Purity Distillation , 2007, IEEE Control Systems.