Robust process model identification in model based control...

Details Robust process model identification in model based control... Robust process model identification in model based control...

2006-04-13

2010-11-23

Barnes-Bullock, Crystal J (Department: 2121)

Data processing: generic control systems or specific application

Generic control system, apparatus or process

Optimization or adaptive control

C700S031000, C700S037000, C700S044000, C700S072000, C700S108000, C702S182000, C702S189000, C703S002000

Reexamination Certificate

active

07840287

ABSTRACT:
A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained. Additionally, process models created using this technique generally have wider confidence intervals, therefore providing a model that works adequately in many process situations without needing to manually or graphically change the model.

REFERENCES:
patent: 5596704 (1997-01-01), Geddes et al.
patent: 5704011 (1997-12-01), Hansen et al.
patent: 5752007 (1998-05-01), Morrison
patent: 5838563 (1998-11-01), Dove et al.
patent: 5877954 (1999-03-01), Klimasauskas et al.
patent: 6056781 (2000-05-01), Wassick et al.
patent: 6102958 (2000-08-01), Meystel et al.
patent: 6103958 (2000-08-01), Tietz
patent: 6445963 (2002-09-01), Blevins et al.
patent: 6484108 (2002-11-01), Burgmayer et al.
patent: 6629012 (2003-09-01), Riley et al.
patent: 6631299 (2003-10-01), Patel et al.
patent: 6693439 (2004-02-01), Liu et al.
patent: 6697767 (2004-02-01), Wang et al.
patent: 6718234 (2004-04-01), Demoro et al.
patent: 6721609 (2004-04-01), Wojsznis et al.
patent: 6757579 (2004-06-01), Pasadyn
patent: 6760716 (2004-07-01), Ganesamoorthi et al.
patent: 6804600 (2004-10-01), Uluyol et al.
patent: 6826521 (2004-11-01), Hess et al.
patent: 6876966 (2005-04-01), Deng et al.
patent: 6957201 (2005-10-01), Alhadef et al.
patent: 6961626 (2005-11-01), Paik
patent: 7085615 (2006-08-01), Persson et al.
patent: 7187989 (2007-03-01), Attarwala
patent: 7209793 (2007-04-01), Harmse et al.
patent: 7251807 (2007-07-01), Melvin et al.
patent: 7349753 (2008-03-01), Paik
patent: 7403834 (2008-07-01), Poolla et al.
patent: 7415382 (2008-08-01), Bickford et al.
patent: 7444191 (2008-10-01), Caldwell et al.
patent: 2002/0022939 (2002-02-01), Senta et al.
patent: 2002/0049575 (2002-04-01), Jalali et al.
patent: 2002/0105428 (2002-08-01), Benson et al.
patent: 2002/0177908 (2002-11-01), Bhat et al.
patent: 2005/0109402 (2005-05-01), Lu
patent: 2005/0210337 (2005-09-01), Chester et al.
patent: 2006/0012064 (2006-01-01), Hutson et al.
patent: 2006/0189009 (2006-08-01), Ahn et al.
patent: 2007/0005266 (2007-01-01), Blevins et al.
patent: 2007/0073539 (2007-03-01), Chengalvarayan et al.
patent: 2007/0078529 (2007-04-01), Thiele et al.
patent: 2009/0112335 (2009-04-01), Mehta et al.
patent: 2009/0198350 (2009-08-01), Thiele
Gustafsson, “Robust Identification of Uncertain Systems,”Research Projects at teh Process Control Laboratory, Abo Akademi University, Sweden (1999).
Ljung, “System Identification: Theory for the User,” ISBN 0-13-881640-9,Prentice-Hall(1987).
Press et al., “Numerical Recepies in C,”Cambridge University Press(1997).
Qin et al., “An Overview of Industrial Model Predictive Control Technology,”Fifth International Conference on Chemical Process Control, pp. 232-256 (1997).
Reinelt et al., “Model Error Modeling in Robust Identification,”Division of Automatic Control Report, Linkopings University, Sweden (2001).
Stachurski et al., “Robust Identification of an Augmented Gurson Model for Elasto-Plastic Porous Media,”XXI ICTAM, Warsaw, Poland (2004).
Wojszins et al., “Developing Confidence Intervals for Process Model Validation,”ISA Technical Conference(2002).
Wojszins et al., “Practical Approach to Tuning MPC,” accepted for publication inISA Transactions(2002).
Wojsznis et al., “Easy Robust Optimal Predictive Controller,”Advances in Instrumentation and Control, ISA 2000 Conference(2000).
Zhu et al., “Parametric Versus Nonparametric Models in MPC Process Identification,”Hydrocarbon Processing(2000).
Search Report under Section 17(5) for Application No. GB0707141.8, dated Jun. 25, 2007.
Notification of First Office Action in Chinese Application No. 200710098116.3 from the State Intellectual Property Office of the P.R. China. (including English translation) dated Jan. 29, 2010.

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