000121582 001__ 121582 000121582 005__ 20241125101125.0 000121582 0247_ $$2doi$$a10.3390/machines11020128 000121582 0248_ $$2sideral$$a132186 000121582 037__ $$aART-2023-132186 000121582 041__ $$aeng 000121582 100__ $$0(orcid)0000-0002-0283-7344$$aPerez-Salesa, Irene$$uUniversidad de Zaragoza 000121582 245__ $$aPrecise Dynamic Consensus under Event-Triggered Communication 000121582 260__ $$c2023 000121582 5060_ $$aAccess copy available to the general public$$fUnrestricted 000121582 5203_ $$aThis work addresses the problem of dynamic consensus, which consists of estimating the dynamic average of a set of time-varying signals distributed across a communication network of multiple agents. This problem has many applications in robotics, with formation control and target tracking being some of the most prominent ones. In this work, we propose a consensus algorithm to estimate the dynamic average in a distributed fashion, where discrete sampling and event-triggered communication are adopted to reduce the communication burden. Compared to other linear methods in the state of the art, our proposal can obtain exact convergence under continuous communication even when the dynamic average signal is persistently varying. Contrary to other sliding-mode approaches, our method reduces chattering in the discrete-time setting. The proposal is based on the discretization of established exact dynamic consensus results that use high-order sliding modes. The convergence of the protocol is verified through formal analysis, based on homogeneity properties, as well as through several numerical experiments. Concretely, we numerically show that an advantageous trade-off exists between the maximum steady-state consensus error and the communication rate. As a result, our proposal can outperform other state-of-the-art approaches, even when event-triggered communication is used in our protocol. 000121582 536__ $$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-124137OB-I00$$9info:eu-repo/grantAgreement/ES/MCIU/FPU20-03134$$9info:eu-repo/grantAgreement/ES/DGA/T45-20R$$9info:eu-repo/grantAgreement/EUR/AEI/TED2021-130224B-I00 000121582 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000121582 590__ $$a2.1$$b2023 000121582 592__ $$a0.474$$b2023 000121582 591__ $$aENGINEERING, MECHANICAL$$b83 / 183 = 0.454$$c2023$$dQ2$$eT2 000121582 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b193 / 353 = 0.547$$c2023$$dQ3$$eT2 000121582 593__ $$aElectrical and Electronic Engineering$$c2023$$dQ2 000121582 593__ $$aComputer Science (miscellaneous)$$c2023$$dQ2 000121582 593__ $$aMechanical Engineering$$c2023$$dQ2 000121582 593__ $$aControl and Systems Engineering$$c2023$$dQ2 000121582 593__ $$aIndustrial and Manufacturing Engineering$$c2023$$dQ2 000121582 593__ $$aControl and Optimization$$c2023$$dQ2 000121582 594__ $$a3.0$$b2023 000121582 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000121582 700__ $$aAldana-Lopez, Rodrigo$$uUniversidad de Zaragoza 000121582 700__ $$0(orcid)0000-0002-3032-954X$$aSagues, Carlos$$uUniversidad de Zaragoza 000121582 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát. 000121582 773__ $$g11, 2 (2023), 128 [19 pp.]$$pMachines (Basel)$$tMachines$$x2075-1702 000121582 8564_ $$s2656280$$uhttps://zaguan.unizar.es/record/121582/files/texto_completo.pdf$$yVersión publicada 000121582 8564_ $$s2690059$$uhttps://zaguan.unizar.es/record/121582/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000121582 909CO $$ooai:zaguan.unizar.es:121582$$particulos$$pdriver 000121582 951__ $$a2024-11-22-11:57:26 000121582 980__ $$aARTICLE