{"id":3056,"date":"2025-10-28T13:54:41","date_gmt":"2025-10-28T16:54:41","guid":{"rendered":"https:\/\/icc.fcen.uba.ar\/?p=3056"},"modified":"2025-10-28T13:54:41","modified_gmt":"2025-10-28T16:54:41","slug":"hybrid-resource-allocation-control-in-cyber-physical-systems-a-novel-simulation-driven-methodology-with-applications-to-uavs","status":"publish","type":"post","link":"https:\/\/icc.fcen.uba.ar\/en\/hybrid-resource-allocation-control-in-cyber-physical-systems-a-novel-simulation-driven-methodology-with-applications-to-uavs\/","title":{"rendered":"Hybrid resource allocation control in cyber-physical systems: a novel simulation-driven methodology with applications to UAVs"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container has-pattern-background has-mask-background nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1144px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:20px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-order-medium:0;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-text fusion-text-1\"><p>Authors: Ezequiel Pecker Marcosig; Juan I. Giribet; Rodrigo D Castro.<\/p>\n<p>Abstract:<br \/>\nDesigning hybrid controllers for cyber-physical systems (CPSs) where computational and physical components influence each other is a challenging task, as it requires considering the performance of very different types of dynamics simultaneously. Meanwhile, controlling each of these dynamics separately can lead to unacceptable results. Common approaches to controller design rely on the use of analytical methods. Although this approach can provide formal guarantees of stability and performance, the analytical design of hybrid controllers can become quite cumbersome. Alternatively, modeling and simulation (M&#038;S)-based design techniques have proven successful for hybrid controllers, providing robust results based on Monte Carlo techniques. This requires simulation models and platforms capable of seamlessly composing the underlying hybrid domains. Unmanned Aerial Vehicles (UAVs) are CPSs with sensitive physical\u2013computational couplings. We address the development of a hybrid model and simulation platform for a data collection application involving UAVs with onboard data processing. The quality of control (QoC) of the physical dynamics must be ensured together with the quality of service (QoS) of the onboard software competing for scarce processing resources. In this scenario, it is imperative to find safe trade-offs between flight stability and processing throughput that can adapt to uncertain environments. The goal is to design a hybrid supervisory controller that dynamically adapts the use of resources to balance the performance of both aspects in a CPS, while ensuring system-level QoS. We present the end-to-end M&#038;S-based design methodology, which can be regarded as a design template for a broader class of CPSs.<\/p>\n<p>More information:<br \/>\n<a href=\"https:\/\/www.researchgate.net\/publication\/389800177_Hybrid_resource_allocation_control_in_cyber-physical_systems_a_novel_simulation-driven_methodology_with_applications_to_UAVs\" target=\"_blank\" rel=\"noopener\">https:\/\/www.researchgate.net\/publication\/389800177_Hybrid_resource_allocation_control_in_cyber-physical_systems_a_novel_simulation-driven_methodology_with_applications_to_UAVs<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":9,"featured_media":3057,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[98],"tags":[],"class_list":["post-3056","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-papers"],"_links":{"self":[{"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/posts\/3056","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/comments?post=3056"}],"version-history":[{"count":1,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/posts\/3056\/revisions"}],"predecessor-version":[{"id":3058,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/posts\/3056\/revisions\/3058"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/media\/3057"}],"wp:attachment":[{"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/media?parent=3056"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/categories?post=3056"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/tags?post=3056"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}