Nonlinear Programming Strategies for State Estimation and Model Predictive Control

@inproceedings{Zavala2009NonlinearPS,
  title={Nonlinear Programming Strategies for State Estimation and Model Predictive Control},
  author={Victor M. Zavala and Lorenz T. Biegler},
  year={2009}
}
Sensitivity-based strategies for on-line moving horizon estimation (MHE) and nonlinear model predictive control (NMPC) are presented both from a stability and computational perspective. These strategies make use of full-space interior-point nonlinear programming (NLP) algorithms and NLP sensitivity concepts. In particular, NLP sensitivity allows us to partition the solution of the optimization problems into background and negligible on-line computations, thus avoiding the problem of… CONTINUE READING
Highly Cited
This paper has 37 citations. REVIEW CITATIONS
26 Extracted Citations
15 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 15 references

Introduction to Sensitivity and Stability Analysis in Nonlinear Programming

  • A. V. Fiacco
  • Academic Press, New York
  • 1983
Highly Influential
4 Excerpts

Fast reduced multiple shooting methods for nonlinear model predictive control

  • A. Schäfer, P. Kühl, M. Diehl, J. Schlöder, H. G. Bock
  • Chemical Engineering and Processing 46, 1200–1214
  • 2007
1 Excerpt

Similar Papers

Loading similar papers…