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A least squares approach to model reduction and the design of low‐order controllers
Author(s) -
Wilson Robert G.,
Seborg Dale E.,
Fisher D. Grant
Publication year - 1976
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450540317
Subject(s) - control theory (sociology) , least squares function approximation , reduction (mathematics) , process (computing) , state space , recursive least squares filter , mathematics , non linear least squares , computer science , mathematical optimization , algorithm , control (management) , estimation theory , statistics , artificial intelligence , geometry , adaptive filter , operating system , estimator
State space process models and feedback control laws are simplified by eliminating selected state variables using a modified least squares technique. The revised technique uses random perturbations to excite the high order system and the resulting reduced‐order models tend to be more robust than those derived using conventional least squares and deterministic inputs. Low‐order controllers were designed for a pilot scale, double effect evaporator using the modified least squares approach. These controllers performed well in both experimental and simulated response tests.