dc.creator | Chevet, T. | es |
dc.creator | Stoica Maniu, Cristina | es |
dc.creator | Vlad, C. | es |
dc.creator | Zhang, Y. | es |
dc.creator | Camacho, E. | es |
dc.creator | Camacho, Eduardo F. | es |
dc.date.accessioned | 2021-09-06T16:00:27Z | |
dc.date.available | 2021-09-06T16:00:27Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Chevet, T., Stoica Maniu, C., Vlad, C., Zhang, Y., Camacho, E. y Camacho, E.F. (2020). Chance-constrained MPC for voronoi-based multi-agent system deployment. En 21st IFAC World Congress 2020, vol. 53, issue 2, Article number 145388, (6969-6974), Berlín: Elsevier B.V. ; IFAC-PapersOnLine. | |
dc.identifier.issn | 2405-8963 | es |
dc.identifier.uri | https://hdl.handle.net/11441/125473 | |
dc.description | Cuenta con otro ed.: IFAC-PapersOnLine
Inluída en vol.53, Issue 2
Article number: 145388 | es |
dc.description.abstract | This paper proposes a new chance-constrained model predictive control (CCMPC) algorithm with state estimation applied to the two-dimensional deployment of a multi-vehicle system where each agent is subject to process noise and measurement noise. The bounded convex area of deployment is partitioned into time-varying Voronoi cells defined by the position of each agent. Due to the presence of noise in the system model, stochastic constraints appear in the model predictive control problem. The proposed decentralized robust CCMPC algorithm drives the multi-agent system into a static Chebyshev configuration where each agent lies on the Chebyshev center of its Voronoi cell. Simulation results show the effectiveness of the proposed control strategy on a fleet of quadrotors subject to wind perturbations and measurement noise. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad ( España) DPI2016- 76493-C3-1-R | es |
dc.description.sponsorship | Feder (UE) 789051 | es |
dc.format | application/pdf | es |
dc.format.extent | 6 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier B.V. | es |
dc.relation.ispartof | 21st IFAC World Congress 2020 (2020), pp. 6969-6974. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Decentralized control | es |
dc.subject | Model predictive control | es |
dc.subject | Multi-agent systems | es |
dc.title | Chance-constrained MPC for voronoi-based multi-agent system deployment | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.projectID | DPI2016- 76493-C3-1-R | es |
dc.relation.projectID | 789051 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S2405896320307072 | es |
dc.identifier.doi | 10.1016/j.ifacol.2020.12.417 | es |
dc.publication.initialPage | 6969 | es |
dc.publication.endPage | 6974 | es |
dc.eventtitle | 21st IFAC World Congress 2020 | es |
dc.eventinstitution | Berlín | es |