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State-Space Interpretation of Model Predictive Control

Lee, Jay H. and Morari, Manfred and Garcia, Carlos E. (1992) State-Space Interpretation of Model Predictive Control. California Institute of Technology , Pasadena, CA. (Unpublished)

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A model predictive control technique based on a step response model is developed using state estimation techniques. The standard step response model is extended so that integrating systems can be treated within the same framework. Based on the modified step response model, it is shown how the state estimation techniques from stochastic optimal control can be used to construct the optimal prediction vector without introducing significant additional numerical complexity. In the case of integrated or double integrated white noise disturbances filtered through general first-order dynamics and white measurement noise, the optimal filter gain is parametrized explicitly in terms of a single parameter between 0 and 1, thus removing the requirement for solving a Riccati equation and equipping the control system with useful on-line tuning parameters. Parallels are drawn to the existing MPC techniques such as Dynamic Matrix Control (DMC), Internal Model Control (IMC) and Generalized Predictive Control (GPC).

Item Type:Report or Paper (Technical Report)
Additional Information:Support from the National Science Foundation and the Petroleum Research Fund administered by the American Chemical Society is gratefully acknowledged.
Group:Control and Dynamical Systems Technical Reports
Record Number:CaltechCDSTR:1992.003
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Usage Policy:You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.
ID Code:28037
Deposited By: Imported from CaltechCDSTR
Deposited On:16 Jul 2006
Last Modified:03 Oct 2019 03:28

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