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Multivariable model state feedback: Computationally simple, easy‐to‐tune alternative to MPC
Author(s) -
Mhatre Suraj,
Brosilow Coleman
Publication year - 2000
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.690460809
Subject(s) - multivariable calculus , control theory (sociology) , decoupling (probability) , filter (signal processing) , model predictive control , process (computing) , computer science , control engineering , engineering , control (management) , artificial intelligence , computer vision , operating system
Methods for the design and tuning of multivariable model state feedback (MMSF) systems for complete and triangular output decoupling of inherently stable processes are presented. MMSF is a method of implementing multivariable IMC controllers that substantially reduces the complexity of the implementation and compensates for control effort saturation. Complexity is reduced because the controls are formed as a linear combination of past and current model states. Saturation‐induced directionality problems are avoided by temporarily increasing filter time constants to bring the control vector into the constraint set. The possibility that needed future controls will be inadequate to compensate for past control actions is avoided by setting minimum values for the controller filter time constants. The feedback structure of MMSF facilitates on‐line tuning, one process variable at a time. Also, because MMSF is a form of IMC, it can be tuned off‐line to accommodate process uncertainty using H ∞ methods.