144944 20260217205529.0 doi 10.1016/j.conengprac.2024.106067 sideral 139740 ART-2024-139740 eng Moya-Lasheras, Eduardo Universidad de Zaragoza (orcid)0000-0001-6244-3366 Iterative-only learning control for soft landing of short-stroke reluctance actuators 2024 Access copy available to the general public Unrestricted Short-stroke reluctance actuators, such as electromechanical relays and solenoid valves, often experience strong impacts at the end of standard switching operations, leading to problems like contact bounces, mechanical wear or acoustic noise. This paper introduces a novel iterative learning controller for tracking the actuator position during switching operations and ultimately decreasing the impact velocities. The control strategy is designed based on a generalized dynamical description, allowing for its application to a broad range of actuators and models. A highlight of the proposal is the absence of a real-time feedback controller, which eliminates the need for real-time measurements. Another important feature is the adaptation of the learning control gain based on a soft-landing performance index. The control performance is analyzed and validated in conjunction with an offline position estimator through Monte Carlo simulations and experimental testing. The results show that the position tracking remains accurate even in the presence of non-repeating perturbations and estimation errors, leading to a substantial reduction in switching impacts. info:eu-repo/grantAgreement/EUR/AEI/CPP2021-008938 info:eu-repo/grantAgreement/EUR/AEI/TED2021-130224B-I00 info:eu-repo/grantAgreement/ES/DGA/T45-23R info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-124137OB-I00 info:eu-repo/semantics/openAccess by-nc https://creativecommons.org/licenses/by-nc/4.0/deed.es 4.6 2024 AUTOMATION & CONTROL SYSTEMS 21 / 89 = 0.236 2024 Q1 T1 ENGINEERING, ELECTRICAL & ELECTRONIC 96 / 368 = 0.261 2024 Q2 T1 1.309 2024 Applied Mathematics 2024 Q1 Electrical and Electronic Engineering 2024 Q1 Control and Systems Engineering 2024 Q1 Computer Science Applications 2024 Q1 9.7 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Sagues, Carlos Universidad de Zaragoza (orcid)0000-0002-3032-954X 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 152 (2024), 106067 [11 pp.] Control eng. pract. CONTROL ENGINEERING PRACTICE 0967-0661 2478584 http://zaguan.unizar.es/record/144944/files/texto_completo.pdf Versión publicada 2678936 http://zaguan.unizar.es/record/144944/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:144944 articulos driver 2026-02-17-20:31:36 ARTICLE