Imperial College London
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ProfessorEricKerrigan

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Control and Optimization
 
 
 
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Contact

 

+44 (0)20 7594 6343e.kerrigan Website

 
 
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Assistant

 

Mrs Raluca Reynolds +44 (0)20 7594 6281

 
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Location

 

1114Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Khusainov:2018:10.1109/TCST.2018.2855666,
author = {Khusainov, B and Kerrigan, EC and Constantinides, G},
doi = {10.1109/TCST.2018.2855666},
journal = {IEEE Transactions on Control Systems Technology},
pages = {2295--2304},
title = {Automatic software and computing hardware co-design for predictive control},
url = {http://dx.doi.org/10.1109/TCST.2018.2855666},
volume = {27},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Model predictive control (MPC) is a computationally demanding control technique that allows dealing with multiple-input and multiple-output systems while handling constraints in a systematic way. The necessity of solving an optimization problem at every sampling instant often 1) limits the application scope to slow dynamical systems and/or 2) results in expensive computational hardware implementations. Traditional MPC design is based on the manual tuning of software and computational hardware design parameters, which leads to suboptimal implementations. This brief proposes a framework for automating the MPC software and computational hardware codesign while achieving an optimal tradeoff between computational resource usage and controller performance. The proposed approach is based on using a biobjective optimization algorithm, namely BiMADS. Two test studies are considered: a central processing unit and field-programmable gate array implementations of fast gradient-based MPC. Numerical experiments show that the optimization-based design outperforms Latin hypercube sampling, a statistical sampling-based design exploration technique.
AU  - Khusainov,B
AU  - Kerrigan,EC
AU  - Constantinides,G
DO  - 10.1109/TCST.2018.2855666
EP  - 2304
PY  - 2018///
SN  - 1063-6536
SP  - 2295
TI  - Automatic software and computing hardware co-design for predictive control
T2  - IEEE Transactions on Control Systems Technology
UR  - http://dx.doi.org/10.1109/TCST.2018.2855666
UR  - https://ieeexplore.ieee.org/document/8423109
UR  - http://hdl.handle.net/10044/1/62030
VL  - 27
ER  -
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