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An experimental evaluation of Kalman filtering
Author(s) -
Hamilton James C.,
Seborg Dale E.,
Fisher D. Grant
Publication year - 1973
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690190503
Subject(s) - kalman filter , control theory (sociology) , noise (video) , invariant extended kalman filter , weighting , extended kalman filter , ensemble kalman filter , fast kalman filter , alpha beta filter , filter (signal processing) , covariance , mathematics , statistics , computer science , moving horizon estimation , control (management) , artificial intelligence , acoustics , physics , image (mathematics) , computer vision
The effectiveness of the stationary form of the discrete Kalman filter for state estimation in noisy process systems was demonstrated by simulated and experimental tests on a pilot plant evaporator. The filter was incorporated into a multivariable, computer control system and resulted in good control despite process and/or measurement noise levels of 10%. The results were significantly better than those obtained when the Kalman filter was omitted or replaced by conventional exponential filters. In this application the standard Kalman filter was reasonably insensitive to incorrect estimates of initial conditions or noise statistics and to errors in model parameters. The filter estimates were sensitive to unmeasured process disturbances. However this sensitivity could be reduced by treating the noise covariance matrices R and Q as design parameters rather than noise statistics and selecting values which result in increased weighting of the process measurements relative to the calculated model states.