Imperial College London
Alternate

Dr Imad M. Jaimoukha

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6279i.jaimouka Website

 
 
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Location

 

617Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Georgiou:2023:10.1109/TAC.2022.3200956,
author = {Georgiou, A and Furqan, T and Jaimoukha, I and Evangelou, SA},
doi = {10.1109/TAC.2022.3200956},
journal = {IEEE Transactions on Automatic Control},
pages = {3822--3829},
title = {Computationally efficient robust model predictive control for uncertain system using causal state-feedback parameterization},
url = {http://dx.doi.org/10.1109/TAC.2022.3200956},
volume = {68},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This paper investigates the problem of robustmodel predictive control (RMPC) of linear-time-invariant (LTI)discrete-time systems subject to structured uncertainty andbounded disturbances. Typically, the constrained RMPCproblem with state-feedback parameterizations is nonlinear(and nonconvex) with a prohibitively high computationalburden for online implementation. To remedy this, a novelapproach is proposed to linearize the state-feedback RMPCproblem, with minimal conservatism, through the use ofsemidefinite relaxation techniques. The proposed algorithmcomputes the state-feedback gain and perturbation onlineby solving a linear matrix inequality (LMI) optimization that,in comparison to other schemes in the literature is shownto have a substantially reduced computational burdenwithout adversely affecting the tracking performance of thecontroller. Additionally, an offline strategy that providesinitial feasibility on the RMPC problem is presented. Theeffectiveness of the proposed scheme is demonstratedthrough numerical examples from the literature.
AU  - Georgiou,A
AU  - Furqan,T
AU  - Jaimoukha,I
AU  - Evangelou,SA
DO  - 10.1109/TAC.2022.3200956
EP  - 3829
PY  - 2023///
SN  - 0018-9286
SP  - 3822
TI  - Computationally efficient robust model predictive control for uncertain system using causal state-feedback parameterization
T2  - IEEE Transactions on Automatic Control
UR  - http://dx.doi.org/10.1109/TAC.2022.3200956
UR  - http://hdl.handle.net/10044/1/98974
VL  - 68
ER  -
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