Difference between revisions of "NCS: Moving Horizon Estimation"

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== Additional Resources ==
 
== Additional Resources ==
 
<!-- Links to additional information. If there are good sources of additional information for students interested in exploring this topic further, these should go at the bottom of the page. -->
 
<!-- Links to additional information. If there are good sources of additional information for students interested in exploring this topic further, these should go at the bottom of the page. -->
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* <p>[http://jbrwww.che.wisc.edu/theses/rao.ps Moving Horizon Strategies for the Constrained Monitoring and Control of Nonlinear Discrete-Time Systems] C.V. Rao. Rao's PhD thesis contains a lot of material on MHE. There is also a discussion on MAP estimates.</p>

Revision as of 18:22, 19 April 2006

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In this lecture, we give an introduction to moving horizon estimation (MHE) and extended Kalman filters (EKF). These filter stuctures can be used with nonlinear models and are therefore more general than the standard Kalman filter. Furthermore, MHE can also take constraints on the noise and the state space, as well as asymmetric probability distributions, into account. MHE is dual to receding horizon control (RHC) and also relies on optimization software. The lecture ends with a brief discussion on stability properties of MHE.

Lecture Materials

Reading

Additional Resources