Computer Architectures to Close the Loop in Real-time Optimization
File(s)
Accepted version
Author(s)
Kerrigan, EC
Constantinides, GA
Suardi, A
Picciau, A
Khusainov, B
Type
Conference Paper
Abstract
Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as `fast' optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other.
Date Issued
2015-12-18
Date Acceptance
2015-07-25
Citation
Proc. 54th IEEE Conference on Decision and Control, 2015, pp.4597-4611
ISBN
978-1-4799-7884-7
Publisher
IEEE
Start Page
4597
End Page
4611
Journal / Book Title
Proc. 54th IEEE Conference on Decision and Control
Copyright Statement
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Siemens AG
Commission of the European Communities
Royal Academy Of Engineering
Imagination Technologies Ltd
Grant Number
EP/G031576/1
EP/I012036/1
EP/I020357/1
EESA_P43327
PITN-GA-2013-607957
Prof Constantinides Chair
Prof Constantinides Chair
Source
54th IEEE Conference on Decision and Control
Publication Status
Published
Start Date
2015-12-15
Finish Date
2015-12-18
Coverage Spatial
Osaka, Japan