Modeling for process control. Final report, February 15, 1991--February 14, 1997

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Explosive developments have taken place in the control field during the last decade. In the content of this work, the authors mention three of these milestones: first, the industrial development and application of model predictive control (MPC), a technique especially suitable for multivariable highly interactive processes involving constraints on manipulated as well as controlled variables. Second, on the theory side, robust control emerged as the eminent paradigm. Robust control aims at analyzing the effects of model uncertainty on closed loop performance and to incorporate insensitivity to model uncertainty into the control system design procedure in a systematic fashion. And third, ... continued below

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11 p.

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Morari, M. December 31, 1997.

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Description

Explosive developments have taken place in the control field during the last decade. In the content of this work, the authors mention three of these milestones: first, the industrial development and application of model predictive control (MPC), a technique especially suitable for multivariable highly interactive processes involving constraints on manipulated as well as controlled variables. Second, on the theory side, robust control emerged as the eminent paradigm. Robust control aims at analyzing the effects of model uncertainty on closed loop performance and to incorporate insensitivity to model uncertainty into the control system design procedure in a systematic fashion. And third, substantial advances have been made in the mathematical theory of the dynamics of nonlinear systems. Various nonlinear control techniques have grown out of this work which have excellent potential to address some difficult and long-standing process control problems. Specifically, the authors have been pursuing (1) the development of linear regression techniques for problems with collinear data and (2) the development of tools for robust model predictive control system design for constrained and nonlinear systems; in particular, they are trying to establish what model uncertainty descriptions are suitable for design.

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11 p.

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OSTI as DE99000064

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  • Other Information: PBD: [1997]

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  • Other: DE99000064
  • Report No.: DOE/ER/14170--T1
  • Grant Number: FG03-91ER14170
  • DOI: 10.2172/666149 | External Link
  • Office of Scientific & Technical Information Report Number: 666149
  • Archival Resource Key: ark:/67531/metadc706119

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  • December 31, 1997

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  • Sept. 12, 2015, 6:31 a.m.

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  • Nov. 5, 2015, 12:50 p.m.

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Morari, M. Modeling for process control. Final report, February 15, 1991--February 14, 1997, report, December 31, 1997; United States. (digital.library.unt.edu/ark:/67531/metadc706119/: accessed August 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.