Disturbance Models for Offset-Free Model-Predictive Control

  title={Disturbance Models for Offset-Free Model-Predictive Control},
  author={Gabriele Pannocchia},
Model predicti®e control algorithms achie®e offset-free control objecti®es by adding integrating disturbances to the process model. The purpose of these additional disturbances is to lump the plant-model mismatch andror unmodeled disturbances. Its effecti®eness has been pro®en for particular square cases only. For systems with a number of ( ) ( ) measured ®ariables p greater than the number of manipulated ®ariables m , it is clear that any controller can track without offset at most m… CONTINUE READING
Highly Influential
This paper has highly influenced 27 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 404 citations. REVIEW CITATIONS
225 Citations
11 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 225 extracted citations

405 Citations

Citations per Year
Semantic Scholar estimates that this publication has 405 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 11 references

‘ Disturbance Modeling for Off - set - Free Linear Model Predictive Control

  • K. R. Muske, T. A. Badgwell
  • 2002

Model Predictive Control of Multi - Rate Sampled - Data Systems : A State - Space Ap - Ž . proach

  • J. H. Lee, M. S. Gelormino, M. Morari
  • Int . J . Control
  • 1992

Minimizing Unobservability in Ž . Inferential Control Schemes

  • J. H. Lee, M. Morari, +6 authors G. Stephanopoulos
  • Int . J . Control
  • 1980

‘ A Note on the Design of Industrial Regulators : Integral Feedback and Feedforward Controllers

  • E. J. Davison, H. W. Smith
  • Ž . Automatica
  • 1974

‘ Design of Industrial Regulators . Integral Feedback and Feedforward Control

  • H. W. Smith
  • 1972

‘ Model Predictive Control with Ž . Linear Models

  • K. R. Muske, J. B. Rawlings

Similar Papers

Loading similar papers…