Instrumentation design based on optimal Kalman filtering

This paper presents a methodology for locating sensors in dynamic systems. It aims to maximize Kalman filtering performance by using accuracy as its main performance index. To accomplish this task, both the measurement noise and the observation matrices are manipulated. The method has been applied in two academic case studies and in the Tennessee Eastman Challenge Problem and has shown promising results. � 2005 Elsevier Ltd. All rights reserved.

[1]  D. Chmielewski,et al.  On the theory of optimal sensor placement , 2002 .

[2]  L. Teng.  Theory and application of Kalman filtering , 1970 .

[3]  Derrick K. Rollins,et al.  Unbiased estimation in dynamic data reconciliation , 1993 .

[4]  Christos Georgakis,et al.  Optimal measurement system design for chemical processes , 2003 .

[5]  W. Ames.  Mathematics in Science and Engineering , 1999 .

[6]  P. Abbeel,et al.  Kalman filtering , 2020, IEEE Control Systems Magazine.

[7]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[8]  N E Manos,et al.  Stochastic Models , 1960, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[9]  Mohamed Darouach,et al.  Data reconciliation in generalized linear dynamic systems , 1991 .

[10]  L. Puigjaner,et al.  Decision-Making Strategy and Tool for Sensor Network Design and Retrofit , 2004 .

[11]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[12]  L. Puigjaner,et al.  Design and Retrofit of Reliable Sensor Networks , 2004 .

[13]  C. M. Crowe,et al.  Observability and redundancy of process data for steady state reconciliation , 1989 .

[14]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

[15]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[16]  Boris Kalitventzeff,et al.  Computer-Aided Design of Redundant Sensor Networks , 2002 .

[17]  M. Bagajewicz,et al.  Instrumentation Design and Upgrade for Principal Components Analysis Monitoring , 2004 .

[18]  L. Biegler,et al.  Data reconciliation and gross‐error detection for dynamic systems , 1996 .

[19]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[20]  Miguel J. Bagajewicz.  Process Plant Instrumentation: Design and Upgrade , 2000 .

[21]  A. Laub,et al.  Numerical linear algebra aspects of control design computations , 1985, IEEE Transactions on Automatic Control.

[22]  Miguel J. Bagajewicz,et al.  Integral approach to plant linear dynamic reconciliation , 1997 .