Fast and accurate method for computing non-smooth solutions to constrained control problems

Conference paper


Nita, L., Vila, E. M. G., Zagorowska, M. A., Kerrigan, E. C., Nie, Y., McInerney, I. and Falugi, P. 2022. Fast and accurate method for computing non-smooth solutions to constrained control problems. European Control Conference (ECC) 2022. London, UK 12 - 15 Jul 2022 IEEE. https://doi.org/10.23919/ECC55457.2022.9838569
AuthorsNita, L., Vila, E. M. G., Zagorowska, M. A., Kerrigan, E. C., Nie, Y., McInerney, I. and Falugi, P.
TypeConference paper
Abstract

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 constraints. State-of-the-art methods use fixed mesh schemes, which cannot achieve superlinear convergence in the presence of non-smooth solutions. In this paper, we propose using a flexible mesh in an integrated residual method. The locations of the mesh nodes are introduced as decision variables, and constraints are added to set upper and lower bounds on the size of the mesh intervals. We compare our approach to a uniform fixed mesh on a real-world satellite reorientation example. This example demonstrates that the flexible mesh enables the solver to automatically locate the discontinuities in the solution, has superlinear convergence and faster solve times

Year2022
ConferenceEuropean Control Conference (ECC) 2022
PublisherIEEE
Accepted author manuscript
License
File Access Level
Anyone
Publication dates
Online05 Aug 2022
Publication process dates
Deposited04 Jul 2023
Journal citationpp. 1049-1054
Book title2022 European Control Conference (ECC)
ISBN9783907144077
9781665497336
FunderEngineering and Physical Sciences Research Council (EPSRC)
Digital Object Identifier (DOI)https://doi.org/10.23919/ECC55457.2022.9838569
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/xpl/conhome/9837955/proceeding
Copyright holder© 2022, The Author(s)
Copyright informationFor the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) license to any Accepted Manuscript version arising
Permalink -

https://repository.uel.ac.uk/item/8w3vv

Download files


Accepted author manuscript
  • 55
    total views
  • 49
    total downloads
  • 5
    views this month
  • 1
    downloads this month

Export as

Related outputs

Machine Learning-Enhanced Benders Decomposition Approach for the Multi-Stage Stochastic Transmission Expansion Planning Problem
Borozan, S., Giannelos, S., Falugi, P., Moreira, A. and Strbac, G. 2024. Machine Learning-Enhanced Benders Decomposition Approach for the Multi-Stage Stochastic Transmission Expansion Planning Problem. Electric Power Systems Research. 237, p. Art. 110985. https://doi.org/10.1016/j.epsr.2024.110985
An integrated planning framework for optimal power generation portfolio including frequency and reserve requirements
Ayo, O, Falugi, P. and Strbac, G 2024. An integrated planning framework for optimal power generation portfolio including frequency and reserve requirements. IET Energy Systems Integration. In Press.
Automatic scenario generation for efficient solution of robust optimal control problems
Zagorowska, M., Falugi, P., O'Dwyer, E. and Kerrigan E. C. 2024. Automatic scenario generation for efficient solution of robust optimal control problems. International Journal of Robust and Nonlinear Control. 34 (2), pp. 1370-1396. https://doi.org/10.1002/rnc.7038
Automatic Scenario Generation for Robust Optimal Control Problems
Zagorowska, M., Falugi, P., O'Dwyer, E. and Kerrigan, E. C. 2023. Automatic Scenario Generation for Robust Optimal Control Problems. IFAC 2023: 22nd World Congress of the International Federation of Automatic Control. Yokohama, Japan 09 - 14 Jul 2023 Elsevier for the International Federation of Automatic Control. https://doi.org/10.1016/j.ifacol.2023.10.1743
Automating the data-driven predictive control design process for building thermal management
Falugi, P., O'Dwyer, E., Shah, N. and Kerrigan, E. C. 2022. Automating the data-driven predictive control design process for building thermal management. ECOS 2022 35th International Conference. Copenhagen, Denmark 03 - 07 Jul 2022 Danmarks Tekniske Universitet (DTU). https://doi.org/10.11581/dtu.00000267
A Modelling Workflow for Predictive Control in Residential Buildings
O’Dwyer, E., Atam, E., Falugi, P., Kerrigan, E. C., Zagorowska, M. A. and Shah, N. 2022. A Modelling Workflow for Predictive Control in Residential Buildings. in: Vahidinasab, V. and Mohammadi-Ivatloo, B. (ed.) Active Building Energy Systems: Operation and Control Springer, Cham. pp. 99-128
MPC and Optimal Design of Residential Buildings with Seasonal Storage: A Case Study
Falugi, P., O’Dwyer, E., Zagorowska, M. A., Atam, E., Kerrigan, E. C., Strbac, G. and Shah, N. 2022. MPC and Optimal Design of Residential Buildings with Seasonal Storage: A Case Study. in: Vahidinasab, V. and Mohammadi-Ivatloo, B. (ed.) Active Building Energy Systems: Operation and Control Springer, Cham. pp. 129-160
Data-Driven Predictive Control With Improved Performance Using Segmented Trajectories
O’Dwyer, E., Kerrigan, E. C., Falugi, P., Zagorowska, M. and Shah, N. 2022. Data-Driven Predictive Control With Improved Performance Using Segmented Trajectories. IEEE Transactions on Control Systems Technology . 31 (3), pp. 1355 - 1365. https://doi.org/10.1109/TCST.2022.3224330
Predictive control co-design for enhancing flexibility in residential housing with battery degradation
Falugi, P., O’Dwyer, E., Kerrigan, E. C., Atam, E., Zagorowska, M. A., Strbac, G. and Shah, N. 2021. Predictive control co-design for enhancing flexibility in residential housing with battery degradation. 7th IFAC Conference on Nonlinear Model Predictive Control NMPC 2021. Bratislava, Slovakia 11 - 14 Jul 2021 Elsevier for the International Federation of Automatic Control. https://doi.org/10.1016/j.ifacol.2021.08.517
Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty
Falugi, P., Giannelos S, Jain A., Borozan S., Moreira A., Bhakar R., Mathur J. and Strbac G. 2021. Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty. Energies. 14 (22), p. 7813. https://doi.org/10.3390/en14227813
Robust and automatic data cleansing method for short-term load forecasting of distribution feeders
Huyghues-Beaufond, N., Tindemans, S., Falugi, P., Sun, M. and Strbac, G. 2020. Robust and automatic data cleansing method for short-term load forecasting of distribution feeders. Applied Energy. 261 (Art. 114405). https://doi.org/10.1016/j.apenergy.2019.114405