Identification of a distributed parameters system for thermo-energetic applications

The distributed parameters systems, represent an active direction of practical and theoretical research, in the control engineering domain, due to the multiple applications in which they can be used [2-12, 30-34] and the rapid development of digital computers and electrical components. The present paper, presents a solution for the identification of a distributed parameters system, represented by a heat exchanger, using the open-loop identification procedures, based on the whitening of the prediction error [1, 13, 14, 15, 16]. In order to choose an optimal identification solution, an analysis of the plant is realized, using the analytical model [43, 44], which is further converted to a numerical form, to search models, for each known adaptation gain used in active control [1, 13, 14, 15, 16]. The choice of all adaptation gains, used in active control is realized in order to describe the heat-exchanger plant, as precisely as possible from the point of view of parameters variation with time.

[1]  Parameter estimation in nonlinear evolution equations , 1998 .

[2]  Han-Xiong Li,et al.  Hybrid intelligent control strategy. Supervising a DCS-controlled batch process , 2001 .

[3]  Michael A. Demetriou,et al.  Integrated Actuator–Sensor Placement and Hybrid Controller Design of Flexible Structures Under Worst Case Spatiotemporal Disturbance Variations , 2004 .

[4]  Emmanuel Witrant,et al.  Shape identification for distributed parameter systems and temperature profiles in tokamaks , 2011, IEEE Conference on Decision and Control and European Control Conference.

[5]  S. O. Reza Moheimani,et al.  Spatial system identification of a simply supported beam and a trapezoidal cantilever plate , 2003, IEEE Trans. Control. Syst. Technol..

[6]  Panagiotis D. Christofides,et al.  Special issue on ‘control of complex process systems’ , 2004 .

[7]  Larry Silverberg,et al.  Modal identification of self‐adjoint distributed‐parameter systems , 1989 .

[8]  Han-Xiong Li,et al.  Comparative study of fluid dispensing modeling , 2003 .

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

[10]  Chin-Shung Hsu.  Digital control systems , 1982, Proceedings of the IEEE.

[11]  Antonios Armaou,et al.  Control of Multiscale and Distributed Process Systems , 2005, Comput. Chem. Eng..

[12]  Suman Banerjee,et al.  Nonlinear Model Reduction Strategies for Rapid Thermal Processing Systems , 1998, ICMTS 1998.

[13]  Fredi Tröltzsch,et al.  Sufficient Second-Order Optimality Conditions for Semilinear Control Problems with Pointwise State Constraints , 2008, SIAM J. Optim..

[14]  Joseph R. Smyth,et al.  MANTLE: a program to calculate a 30 kbar norm assemblage (in terrestrial planet mantles). , 1981 .

[15]  Gheorghe Craciun,et al.  A distributed parameter identification problem in neuronal cable theory models. , 2005, Mathematical biosciences.

[16]  F. L. Dimet,et al.  Sensitivity analysis and parameter estimation for distributed hydrological modeling: potential of variational methods , 2009 .

[17]  Maeve L. McCarthy,et al.  The Identification of a Time Dependent Sorption Parameter from Soil Column Experiments , 2006, SIAM J. Appl. Math..

[18]  Hung-Yuan Chung,et al.  Parameter identification of linear distributed systems via Taylor operational matrix , 1988 .

[19]  Stephen A. Billings,et al.  IDENTIFICATION OF FINITE-DIMENSIONAL MODELS FOR DISTRIBUTED-PARAMETER SYSTEMS , 2002 .

[20]  Han-Xiong Li,et al.  A Simple Model-Based Approach for Fluid Dispensing Analysis and Control , 2007, IEEE/ASME Transactions on Mechatronics.

[21]  N. U. Ahmed,et al.  Parameter identification for partially observed diffusions , 1992 .

[22]  Mariana Todorova,et al.  Genetic algorithm based identification of linear distributed parameter systems by finite difference technique , 2003, CompSysTech '03.

[23]  Ben G. Fitzpatrick,et al.  Estimation of time dependent parameters in general parabolic evolution systems , 1996 .

[24]  Dimitry Gorinevsky,et al.  IEEE Transactions on control systems technology special issue on control of industrial spatially distributed processes , 2003 .

[25]  Ben G. Fitzpatrick,et al.  Estimation of discontinuous parameters in general nonautonomous parabolic systems , 1996, Kybernetika.

[26]  A. Niemist.  Numerical Methods for Nonlinear Inverse Problems , 1996 .

[27]  Daizhan Cheng,et al.  Parameter identification for a class of abstract nonlinear parabolic distributed parameter systems , 2004 .

[28]  Xuecheng Tai,et al.  Parameter Estimation with the Augmented Lagrangian Method for a Parabolic Equation , 2005 .

[29]  Xuecheng Tai,et al.  Permeability Estimation with the Augmented Lagrangian Method for a Nonlinear Diffusion Equation , 2003 .

[31]  S. O. Reza Moheimani,et al.  Spatial resonant control of flexible structures-application to a piezoelectric laminate beam , 2001, IEEE Trans. Control. Syst. Technol..

[32]  B. R. Noack.  Turbulence, Coherent Structures, Dynamical Systems and Symmetry , 2013 .

[33]  Gustavo Belforte,et al.  LPV approximation of distributed parameter systems in environmental modelling , 2005, Environ. Model. Softw..

[34]  Jean-Pierre Raymond,et al.  Hamiltonian Pontryagin's Principles for Control Problems Governed by Semilinear Parabolic Equations , 1999 .

[35]  Harvey Thomas Banks,et al.  An approximation theory for the identification of nonlinear distributed parameter systems , 1990 .

[36]  Sondipon Adhikari,et al.  Distributed parameter model updating using the Karhunen–Loève expansion , 2010 .

[37]  Lennart Ljung.  Parametric Methods for Identification of Transfer Functions of Linear Systems , 1987 .

[38]  Chenkun Qi,et al.  Modeling of distributed parameter systems for applications—A synthesized review from time–space separation , 2010 .

[39]  IDENTIFICATION OF CONSTANT PARAMETERS IN PERTURBED SINE-GORDON EQUATIONS , 2006 .

[40]  Panagiotis D. Christofides,et al.  Special volume on ‘control of distributed parameter systems’ , 2002 .