A two-timescale approach to nonlinear Model Predictive Control

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Model Predictive Control (MPC) schemes generate controls by using a model to predict the plant`s response to various control strategies. A problem arises when the underlying model is obtained by fitting a general nonlinear function, such as a neural network, to data: an exorbitant amount of data may be required to obtain accurate enough predictions. We describe a means of avoiding this problem that involves a simplified plant model which bases its predictions on averages of past control inputs. This model operates on a timescale slower than- the rate at which the controls are updated and the plant outputs are ... continued below

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18 p.

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Buescher, K.L. & Baum, C.C. October 1, 1994.

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Description

Model Predictive Control (MPC) schemes generate controls by using a model to predict the plant`s response to various control strategies. A problem arises when the underlying model is obtained by fitting a general nonlinear function, such as a neural network, to data: an exorbitant amount of data may be required to obtain accurate enough predictions. We describe a means of avoiding this problem that involves a simplified plant model which bases its predictions on averages of past control inputs. This model operates on a timescale slower than- the rate at which the controls are updated and the plant outputs are sampled. Not only does this technique give better closed-loop performance from the same amount of open-loop data, but it requires far less on-line computation as well. We illustrate the usefulness of this two-timescale approach by applying it to a simulated exothermic continuously stirred tank reactor with jacket dynamics.

Physical Description

18 p.

Notes

OSTI as DE95000943

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  • 1995 American control conference, Seattle, WA (United States), Jun 1995

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  • Other: DE95000943
  • Report No.: LA-UR--94-3256
  • Report No.: CONF-950653--1
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 39791
  • Archival Resource Key: ark:/67531/metadc685407

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  • October 1, 1994

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

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  • Feb. 26, 2016, 9:31 p.m.

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Buescher, K.L. & Baum, C.C. A two-timescale approach to nonlinear Model Predictive Control, article, October 1, 1994; New Mexico. (digital.library.unt.edu/ark:/67531/metadc685407/: accessed September 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.