Difference between revisions of "NCS: Moving Horizon Estimation"

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<!-- Sample lecture link: * [[Media:L1-1_Intro.pdf|Lecture: Networked Control Systems: Course Overview]] -->
 
<!-- Sample lecture link: * [[Media:L1-1_Intro.pdf|Lecture: Networked Control Systems: Course Overview]] -->
 
* [[Media:L4-2_MHE.pdf|Lecture: Moving Horizon Estimation]]
 
* [[Media:L4-2_MHE.pdf|Lecture: Moving Horizon Estimation]]
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* [[Media:Stateestim.pdf|Lecture notes: State estimation]]
  
 
== Reading ==
 
== Reading ==

Latest revision as of 04:52, 1 May 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

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