Estimation-based Norm-optimal Iterative Learning Control
2014 (English)In: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 73, 76-80 p.Article in journal (Refereed) Published
The norm-optimal iterative learning control (ilc) algorithm for linear systems is extended to an estimation-based norm-optimal ilc algorithm where the controlled variables are not directly available as measurements. A separation lemma is presented, stating that if a stationary Kalman filter is used for linear time-invariant systems then the ilc design is independent of the dynamics in the Kalman filter. Furthermore, the objective function in the optimisation problem is modified to incorporate the full probability density function of the error. Utilising the Kullback–Leibler divergence leads to an automatic and intuitive way of tuning the ilc algorithm. Finally, the concept is extended to non-linear state space models using linearisation techniques, where it is assumed that the full state vector is estimated and used in the ilc algorithm. Stability and convergence properties for the proposed scheme are also derived.
Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 73, 76-80 p.
Iterative learning control; Estimation; Filtering; Non-linear systems
IdentifiersURN: urn:nbn:se:liu:diva-104791DOI: 10.1016/j.sysconle.2014.08.007ISI: 000345108000010OAI: oai:DiVA.org:liu-104791DiVA: diva2:699075
ProjectsVinnova Excellence Center LINK-SICExcellence Center at Linköping-Lund in Information Technology, ELLIITSSF project Collaborative Localization
FunderVINNOVAeLLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications