000147758 001__ 147758 000147758 005__ 20250103153613.0 000147758 0247_ $$2doi$$a10.1109/TAC.2019.2931836 000147758 0248_ $$2sideral$$a119066 000147758 037__ $$aART-2020-119066 000147758 041__ $$aeng 000147758 100__ $$0(orcid)0000-0002-7093-228X$$aJúlvez, Jorge$$uUniversidad de Zaragoza 000147758 245__ $$aSteady State Analysis of Flexible Nets 000147758 260__ $$c2020 000147758 5060_ $$aAccess copy available to the general public$$fUnrestricted 000147758 5203_ $$aThe modeling and analysis of complex dynamic systems, such as those in manufacturing, logistics, and biology, require powerful analysis methods for their study and optimization. A significant modeling and analysis challenge posed by both, artificial and natural systems, is the existence of uncertain parameters. Flexible Nets (FNs) is a novel modeling formalism, inspired by Petri nets, that can handle different types of uncertain parameters in a natural way. This paper develops an efficient method to analyse the evolution of a system modeled by an FN in the long run. More precisely, the method focuses on the computation of steady state bounds for an objective function of interest. The method makes use of a set of constraints, expressed as linear inequalities, that the state variables must satisfy in the steady state. In order to account for systems that do not reach a constant steady state, the developed constraints allow the system state to switch among different values, i.e., the steady state variables are not forced to be constant. 000147758 536__ $$9info:eu-repo/grantAgreement/EC/FP7/289126/EU/BIO knowLEDGe Extractor and Modeller for Protein Production/BIOLEDGE$$9info:eu-repo/grantAgreement/EC/FP7/623995/EU/Protein Synthesis Control by Means of Formal Models/FORMALBIO 000147758 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000147758 590__ $$a5.792$$b2020 000147758 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b34 / 273 = 0.125$$c2020$$dQ1$$eT1 000147758 591__ $$aAUTOMATION & CONTROL SYSTEMS$$b11 / 63 = 0.175$$c2020$$dQ1$$eT1 000147758 592__ $$a3.435$$b2020 000147758 593__ $$aComputer Science Applications$$c2020$$dQ1 000147758 593__ $$aElectrical and Electronic Engineering$$c2020$$dQ1 000147758 593__ $$aControl and Systems Engineering$$c2020$$dQ1 000147758 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000147758 700__ $$aOliver, Stephen G. 000147758 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000147758 773__ $$g65, 6 (2020), 2510-2525$$pIEEE trans. automat. contr.$$tIEEE TRANSACTIONS ON AUTOMATIC CONTROL$$x0018-9286 000147758 8564_ $$s457296$$uhttps://zaguan.unizar.es/record/147758/files/texto_completo.pdf$$yPostprint 000147758 8564_ $$s3592327$$uhttps://zaguan.unizar.es/record/147758/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000147758 909CO $$ooai:zaguan.unizar.es:147758$$particulos$$pdriver 000147758 951__ $$a2025-01-03-13:20:55 000147758 980__ $$aARTICLE