A data-centric system identification approach to input signal design for Hammerstein systems

This paper examines the design of input signals for identification of Hammerstein systems in a data-centric framework by addressing the optimal distribution of regressors. Data-centric estimation methods such as Model-on-Demand (MoD) generate local function approximations from a database of regressors at the current operating point. The data-centric input signal design formulation aims to develop sufficient support in the regressor space for the MoD estimator, while addressing time-domain constraints on the input and output signals. A numerical example is shown to highlight the benefit of proposed design over classical Pseudo Random Binary Sequence (PRBS), Multi Level Pseudo Random Sequence (MLPRS) and uniform random input designs.

[1]  Masakazu Muramatsu,et al.  Sums of Squares and Semidefinite Programming Relaxations for Polynomial Optimization Problems with Structured Sparsity , 2004 .

[2]  E. Bai,et al.  Block Oriented Nonlinear System Identification , 2010 .

[3]  Xavier Bombois,et al.  Optimal experiment design for open and closed-loop system identification , 2011, Commun. Inf. Syst..

[4]  Hyunjin Lee,et al.  Constrained multisine input signals for plant-friendly identification of chemical process systems , 2009 .

[5]  Jorge Nocedal,et al.  An Interior Point Algorithm for Large-Scale Nonlinear Programming , 1999, SIAM J. Optim..

[6]  D. Rivera,et al.  Using engineering control principles to inform the design of adaptive interventions: a conceptual introduction. , 2007, Drug and alcohol dependence.

[7]  Henrik Ohlsson,et al.  Direct Weight Optimization applied to discontinuous functions , 2008, 2008 47th IEEE Conference on Decision and Control.

[8]  Lennart Ljung,et al.  Nonlinear black-box modeling in system identification: a unified overview , 1995, Autom..

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

[10]  Lennart Ljung,et al.  Perspectives on system identification , 2010, Annu. Rev. Control..

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

[12]  Anders Stenman,et al.  Model on Demand: Algorithms, Analysis and Applications , 1999 .

[13]  Hyunjin Lee,et al.  "Plant-Friendly" system identification: a challenge for the process industries , 2003 .

[14]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[15]  Daniel E. Rivera,et al.  Constrained Optimal Input Signal Design for Data-Centric Estimation Methods , 2014, IEEE Transactions on Automatic Control.

[16]  Daniel E. Rivera,et al.  A 'Model-on-Demand' identification methodology for non-linear process systems , 2001 .

[17]  Jean B. Lasserre,et al.  Global Optimization with Polynomials and the Problem of Moments , 2000, SIAM J. Optim..

[18]  H. Hjalmarsson,et al.  Optimal Input Design for Identification of Non-linear Systems: Learning From the Linear Case , 2007, 2007 American Control Conference.

[19]  Daniel E. Rivera,et al.  Optimal input signal design for data-centric estimation methods , 2013, 2013 American Control Conference.

[20]  Paul O'Shea,et al.  Future medicine shaped by an interdisciplinary new biology , 2012, The Lancet.

[21]  Daniel E. Rivera,et al.  System identification: A Wiener-Hammerstein benchmark , 2012 .

[22]  Daniel E. Rivera,et al.  Towards Patient-Friendly Input Signal Design for Optimized Pain Treatment Interventions , 2012 .

[23]  Jean B. Lasserre,et al.  Convergent SDP-Relaxations in Polynomial Optimization with Sparsity , 2006, SIAM J. Optim..