Zobrazeno 1 - 10
of 169
pro vyhledávání: '"Eric C. Kerrigan"'
Autor:
José A. Solís-Lemus, Edward Costar, Denis Doorly, Eric C. Kerrigan, Caroline H. Kennedy, Frances Tait, Steven Niederer, Peter E. Vincent, Steven E. Williams
Publikováno v:
Royal Society Open Science, Vol 7, Iss 8 (2020)
The potential for acute shortages of ventilators at the peak of the COVID-19 pandemic has raised the possibility of needing to support two patients from a single ventilator. To provide a system for understanding and prototyping designs, we have devel
Externí odkaz:
https://doaj.org/article/c807a2f4f9f0425a85e2ad7e9c5179cf
Autor:
Alessandro Ravera, Alberto Oliveri, Matteo Lodi, Alberto Bemporad, W. P. M. H. Heemels, Eric C. Kerrigan, Marco Storace
Several software tools are available in the literature for the design and embedded implementation of linear model predictive control (MPC), both in its implicit and explicit (either exact or approximate) forms. Most of them generate C code for easy i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c27c4fb109fb8ea2444e3abf98d1c95
http://hdl.handle.net/10044/1/103412
http://hdl.handle.net/10044/1/103412
Autor:
Lucian Nita, Eduardo M. G. Vila, Marta A. Zagorowska, Eric C. Kerrigan, Yuanbo Nie, Ian McInerney, Paola Falugi
Publikováno v:
European Control Conference
Introducing flexibility in the time-discretisation mesh can improve convergence and computational time when solving differential equations numerically, particularly when the solutions are discontinuous, as commonly found in control problems with cons
Autor:
Martin P. Neuenhofen, Eric C. Kerrigan
We present a numerical method for the minimization of constrained optimization problems where the objective is augmented with large quadratic penalties of inconsistent equality constraints. Such objectives arise from quadratic integral penalty method
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90ac8ddd2142d1dae418a88c6dbcba3a
http://hdl.handle.net/10044/1/98243
http://hdl.handle.net/10044/1/98243
A class of data-driven control methods has recently emerged based on Willems' fundamental lemma. Such methods can ease the modelling burden in control design but can be sensitive to disturbances acting on the system under control. In this paper, we p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::024a70d8c618a23f733e800469313825
http://hdl.handle.net/10044/1/95254
http://hdl.handle.net/10044/1/95254
Autor:
Ian McInerney, Eric C. Kerrigan
Publikováno v:
13th Symposium on Advances in Control Education
Including Model Predictive Control (MPC) in the undergraduate/graduate control curriculum is becoming vitally important due to the growing adoption of MPC in many industrial areas. In this paper, we present an overview of the predictive control cours
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::293907eebd57367860ad7cc73500aa08
http://arxiv.org/abs/2202.00157
http://arxiv.org/abs/2202.00157
Publikováno v:
IEEE Communications Letters
In networks of mobile autonomous agents, e.g. for data acquisition, we may wish to maximize data transfer or to reliably transfer a minimum amount of data, subject to quality of service or energy constraints. These requirements can be guaranteed thro
Autor:
Eric C. Kerrigan, Yuanbo Nie
Publikováno v:
IEEE Control Systems Letters. 4:61-66
We show via examples that, when solving optimal control problems, representing the optimal state and input trajectory directly using interpolation schemes may not be the best choice. Due to the lack of considerations for solution trajectories in-betw
Publikováno v:
7th IFAC Conference on Nonlinear Model Predictive Control (NMPC)
We use nonlinear model predictive control to procure a joint control of mobility and transmission to minimize total network communication energy use. The nonlinear optimization problem is solved numerically in a self-triggered framework, where the ne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa1069fc58b5f6855ddfcb319c891a83
http://hdl.handle.net/10044/1/92489
http://hdl.handle.net/10044/1/92489
Publikováno v:
Mcinerney, I, Nita, L, Nie, Y, Oliveri, A & Kerrigan, E C 2021, ' Towards a Framework for Nonlinear Predictive Control using Derivative-Free Optimization ', IFAC-PapersOnLine, vol. 54, no. 6, pp. 284-289 . https://doi.org/10.1016/j.ifacol.2021.08.558
7th IFAC Conference on Nonlinear Model Predictive Control
7th IFAC Conference on Nonlinear Model Predictive Control
The use of derivative-based solvers to compute solutions to optimal control problems with non-differentiable cost or dynamics often requires reformulations or relaxations that complicate the implementation or increase computational complexity. We pre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd2277662c876af7090a2b19c6db2f83
https://linkinghub.elsevier.com/retrieve/pii/S2405896321013331
https://linkinghub.elsevier.com/retrieve/pii/S2405896321013331