Imperial College London
Portrait Picture

ProfessorEricKerrigan

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Control and Optimization
 
 
 
//

Contact

 

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

 
 
//

Assistant

 

Mrs Raluca Reynolds +44 (0)20 7594 6281

 
//

Location

 

1114Electrical EngineeringSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@inproceedings{Faqir:2021:10.1016/j.ifacol.2020.12.060,
author = {Faqir, O and Kerrigan, E},
doi = {10.1016/j.ifacol.2020.12.060},
pages = {6516--6521},
publisher = {IFAC Secretariat},
title = {Mesh refinement for event-triggered nonlinear model predictive control},
url = {http://dx.doi.org/10.1016/j.ifacol.2020.12.060},
year = {2021}
}
Download

RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We consider the effect of using approximate system predictions in event-triggeredcontrol  schemes.  Such  approximations  often  result  from  numerical  transcription  methods  forsolving continuous-time optimal control problems. Mesh refinement schemes guarantee upperbounds on the error in the differential equations used to model system dynamics. In particular,we show that with the accuracy guarantees of a mesh refinement scheme, then event-triggeringschemes based on bounding the difference between predicted and measured state can be usedwith a guaranteed strictly positive inter-update time. We determine a lower bound for this timeand show that additional knowledge of the employed transcription method and evaluation ofthe approximation errors may be used to obtain better online estimates of inter-update times.This is the first work to consider using the solution accuracy of an optimal control problem asa metric for triggering new control updates.
AU  - Faqir,O
AU  - Kerrigan,E
DO  - 10.1016/j.ifacol.2020.12.060
EP  - 6521
PB  - IFAC Secretariat
PY  - 2021///
SN  - 2405-8963
SP  - 6516
TI  - Mesh refinement for event-triggered nonlinear model predictive control
UR  - http://dx.doi.org/10.1016/j.ifacol.2020.12.060
UR  - https://www.sciencedirect.com/science/article/pii/S2405896320303165
UR  - http://hdl.handle.net/10044/1/79363
ER  -
Download