Imperial College London
Portrait Picture

Dr Dante Kalise

Faculty of Natural Sciences, Department of Mathematics

Reader in Computational Optimisation and Control
 
 
 
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Contact

 

d.kalise-balza Website CV

 
 
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Location

 

742Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Dolgov:2022:10.1016/j.ifacol.2022.11.104,
author = {Dolgov, S and Kalise, D and Saluzzi, L},
doi = {10.1016/j.ifacol.2022.11.104},
pages = {510--515},
publisher = {ELSEVIER},
title = {Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control},
url = {http://dx.doi.org/10.1016/j.ifacol.2022.11.104},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - An optimized variant of the State Dependent Riccati Equations (SDREs) approach for nonlinear optimal feedback stabilization is presented. The proposed method is based on the construction of equivalent semilinear representations associated to the dynamics and their affine combination. The optimal combination is chosen to minimize the discrepancy between the SDRE control and the optimal feedback law stemming from the solution of the corresponding Hamilton Jacobi Bellman (HJB) equation. Numerical experiments assess effectiveness of the method in terms of stability of the closed-loop with near-to-optimal performance.
AU  - Dolgov,S
AU  - Kalise,D
AU  - Saluzzi,L
DO  - 10.1016/j.ifacol.2022.11.104
EP  - 515
PB  - ELSEVIER
PY  - 2022///
SN  - 2405-8963
SP  - 510
TI  - Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control
UR  - http://dx.doi.org/10.1016/j.ifacol.2022.11.104
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000889050900086&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://www.sciencedirect.com/science/article/pii/S2405896322027379?via%3Dihub
UR  - http://hdl.handle.net/10044/1/101855
ER  -
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