@inproceedings{Khusainov:2017:10.1109/ECC.2016.7810272,
author = {Khusainov, B and Kerrigan, EC and Constantinides, GA},
doi = {10.1109/ECC.2016.7810272},
pages = {110--115},
publisher = {IEEE},
title = {Multi-objective Co-design for Model Predictive Control with an FPGA},
url = {http://dx.doi.org/10.1109/ECC.2016.7810272},
year = {2017}
}

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TY  - CPAPER
AB  - In order to achieve the best possible performanceof a model predictive controller (MPC) for a given set ofresources, the software algorithm and computational platformhave to be designed simultaneously. Moreover, in practicalapplications the controller design problem has a multi-objectivenature: performance is traded off against computational hardwareresource usage, namely time, energy and space. Thispaper proposes formulating an MPC design problem as a multiobjectiveoptimization (MOO) problem in order to explore thedesign trade-offs in a systematic way.Since the design objectives in the resulting MOO problem areexpensive to evaluate, i.e. evaluation requires time consumingsimulations, most of the classical and evolutionary MOOalgorithms cannot be employed for this class of design problems.For this reason a practical MOO algorithm that can deal withexpensive-to-evaluate functions is presented. The algorithm isbased on Kriging and the hypervolume criterion that wasrecently proposed in the expensive optimization literature. Anumerical example for a fast gradient-based controller designshows that the proposed approach can efficiently exploreoptimal performance-resource trade-offs.
AU  - Khusainov,B
AU  - Kerrigan,EC
AU  - Constantinides,GA
DO  - 10.1109/ECC.2016.7810272
EP  - 115
PB  - IEEE
PY  - 2017///
SP  - 110
TI  - Multi-objective Co-design for Model Predictive Control with an FPGA
UR  - http://dx.doi.org/10.1109/ECC.2016.7810272
UR  - http://hdl.handle.net/10044/1/30637
ER  - 

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