Robust MHE - Moving horizon estimation: robust stability, robust performance and interaction with model predictive control
From 01-01-2008 to 31-12-2011
Description
The aim of this project is the development of robust moving horizon estimation (MHE) methods.- Estimating model states and model parameters is crucial for a successful model predictive control (MPC) strategy. They need to be estimated based on measurements and a model. The performance of the closed loop system is directly influenced by the quality of the state estimates.
- The idea behind moving horizon estimation (MHE) is to estimate a sequence of unknown parameters based on a weighted least squares cost function and using a finite optimization window. In the case of a linear system and in the absence of constraints, one can show that MHE is equivalent to the Kalman filter for certain weighting matrices. MHE methods have been developed to overcome the limitations of the widely used Kalman filter, being (i) its inability to incorporate constraints on state and disturbance variables and (ii) the assumptions of Gaussian disturbances and a linear model.
- Over the last years the research of MHE has resulted in important theoretical advances with proofs of stability and optimality. For a successful application of MHE, it is very important to guarantee stability and performance even in the presence of external disturbances.
- Furthermore, our research group is experienced in the simultaneous estimation of states and unknown inputs. Extensions of these recursive formulations towards moving horizon formulations form the second main objective of this project.
- Because of their mutual dependence, state estimation and control cannot be seen separately. The third main objective of this project is therefore the connection of the robust MHE techniques with the control problem.
Objectives
This project has the following main objectives:
a. Develop MHE algorithms with guaranteed robust stability and performance in the presence of model uncertainties.
b. Develop MHE algorithms for the simultaneous estimation of states and unknown inputs with guaranteed robust stability and performance.
c. Develop algorithms for MPC that consider the interaction with MHE explicitly.
Team
- Moritz Diehl, Co-promoter
- Bart De Moor, Promoter
Financing
Funding: FWO - Research Foundation - Flanders
Program/Grant Type: FWO Research Grant - FWO Research Grant
Events
2/09/2024:
PhD defense - Martijn Oldenhof
Machine Learning for Advanced Chemical Analysis and Structure Recognition in Drug Discovery
3/09/2024:
Meet the Jury Igor Tetko on Advanced Machine Learning in Drug Discovery
12/09/2024:
Multimodal analysis of cell-free DNA for sensitive cancer detection in low-coverage and low-sample settings
Seminar by Antoine Passemiers
News
STADIUS Alumni Herman Verrelst – new CEO of Biocartis
08 June 2017
Herman Verrelst, the founder of KU Leuven spin-off Cartagenia, who has been working in Silicon Valley, US for the last few years will be returning to Belgium to follow the steps of Rudi Pauwels as CEO of the Belgian diagnostic company, Biocartis.
Supporting healthcare policymaking via machine learning – batteries included!
29 May 2017
STADIUS takes the lead in the data analytics efforts in an ambitious European Project MIDAS.
Marc Claesen gives an interview about his PhD for the magazine of the Faculty of Engineering Sciences "Geniaal"
10 February 2017
Did you know that in Belgium approximately one third of type 2 diabetes patients are unaware of their condition?
Joos Vandewalle is nieuwe voorzitter KVAB
09 October 2016
Op 5 oktober 2016 heeft de Algemene Vergadering van de Academie KVAB Joos Vandewalle verkozen tot voorzitter van de KVAB.