A state space formulation for model predictive control

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 {Δu(...

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Published in:AIChE journal 1989-02, Vol.35 (2), p.241-249
Main Authors: Li, Sifu, Lim, Kian Y., Fisher, D. Grant
Format: Article
Language:English
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Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization
Applied sciences
Chemical engineering
Exact sciences and technology
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cited_by cdi_FETCH-LOGICAL-c4838-b1d0bde2b230052fb81fb1c835326848944226cefa11d65ae13978ee117d313e3
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description 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 {Δu(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.
doi_str_mv 10.1002/aic.690350208
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subjects Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization
Applied sciences
Chemical engineering
Exact sciences and technology
title A state space formulation for model predictive control
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