Abstract
Moving horizon estimation (MHE) is a state estimation method that is particularly useful for nonlinear or constrained dynamic systems for which few general methods with established properties are available. This entry explains the concept of full information estimation and introduces moving horizon estimation as a computable approximation of full information. The basic design methods for ensuring stability of MHE are presented. The relationships of full information and MHE to other state estimation methods such as Kalman filtering and statistical sampling are discussed.
Similar content being viewed by others
Bibliography
Bertsekas DP (1995) Dynamic programming and optimal control, vol 1. Athena Scientific, Belmont
Kuhl P, Diehl M, Kraus T, Schloder JP, Bock HG (2011) A real-time algorithm for moving horizon state and parameter estimation. Comput Chem Eng 35:71–83
Kwakernaak H, Sivan R (1972) Linear optimal control systems. Wiley, New York. ISBN:0-471-51110-2
Lopez-Negrete R, Biegler LT (2012) A moving horizon estimator for processes with multi-rate measurements: a nonlinear programming sensitivity approach. J Process Control 22:677–688
Patwardhan SC, Narasimhan S, Jagadeesan P, Gopaluni B, Shah SL (2012) Nonlinear Bayesian state estimation: a review of recent developments. Control Eng Pract 20:933–953
Rao CV, Rawlings JB, Mayne DQ (2003) Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations. IEEE Trans Autom Control 48(2): 246–258
Rawlings JB, Bakshi BR (2006) Particle filtering and moving horizon estimation. Comput Chem Eng 30:1529–1541
Rawlings JB, Ji L (2012) Optimization-based state estimation: current status and some new results. J Process Control 22:1439–1444
Rawlings JB, Mayne DQ (2009) Model predictive control: theory and design. Nob Hill Publishing, Madison, 576 p. ISBN:978-0-9759377-0-9
Sontag ED, Wang Y (1997) Output-to-state stability and detectability of nonlinear systems. Syst Control Lett 29:279–290
Zavala VM, Biegler LT (2009) Optimization-based strategies for the operation of low-density polyethylene tubular reactors: nonlinear model predictive control. Comput Chem Eng 33(10):1735–1746
Zavala VM, Laird CD, Biegler LT (2008) A fast moving horizon estimation algorithm based on nonlinear programming sensitivity. J Process Control 18:876–884
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag London
About this entry
Cite this entry
Rawlings, J.B. (2013). Moving Horizon Estimation. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_4-1
Download citation
DOI: https://doi.org/10.1007/978-1-4471-5102-9_4-1
Received:
Accepted:
Published:
Publisher Name: Springer, London
Online ISBN: 978-1-4471-5102-9
eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering
Publish with us
Chapter history
-
Latest
Moving Horizon Estimation- Published:
- 21 November 2020
DOI: https://doi.org/10.1007/978-1-4471-5102-9_4-2
-
Original
Moving Horizon Estimation- Published:
- 07 February 2014
DOI: https://doi.org/10.1007/978-1-4471-5102-9_4-1