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A state space formulation for model predictive control
Author(s) -
Li Sifu,
Lim Kian Y.,
Fisher D. Grant
Publication year - 1989
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.690350208
Subject(s) - control theory (sociology) , setpoint , model predictive control , trajectory , kalman filter , impulse response , state space representation , parametric statistics , computer science , state space , computation , mathematics , algorithm , control (management) , physics , artificial intelligence , mathematical analysis , statistics , astronomy
Model predictive control (MPC) schemes such as MOCCA, DMC, MAC, MPHC, and IMC use discrete step (or impulse) response data rather than a parametric model. They predict the future output trajectory of the process {ŷ( k + i ), i = 1, …, P }, then the controller calculates the required control action {Δ u ( k + i ), i = 0, 1, …, M − 1} so that the difference between the predicted trajectory and user‐specified (setpoint) trajectory is minimized. This paper shows how the step (impulse) response model can be put into state space form thus reducing computation time and permitting the use of state space theorems and techniques with any of the above‐mentioned MPC schemes. A series of experimental runs on a simple pilot plant shows that a Kalman filter based on the proposed state space model gives better performance that direct use of the step response data for prediction.