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. Technical Report. California Institute of Technology, Pasadena, CA. [CaltechCDSTR:1992.003]

Full text available as:

PDF - Requires Adobe Acrobat Reader or other PDF viewer.

Abstract

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).

EPrint Type:	Monograph (Technical Report)
Additional Information:	Support from the National Science Foundation and the Petroleum Research Fund administered by the American Chemical Society is gratefully acknowledged.
Subjects:	All Records
ID Code:	62
Deposited By:	Caltech Library System
Deposited On:	16 July 2006
Unique Identifier:	CaltechCDSTR:1992.003
Official Persistent URL:	http://resolver.caltech.edu/CaltechCDSTR:1992.003
Usage Policy:	You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.

Archive Staff Only: edit this record