{"refrec":{"BRefID":449314,"RR":"<b>Wang, W.; Xiao, W.; Gonzalez-Garcia, A.; Swevers, J.; Ratti, C.; Rus, D.</b> (2024). Robust Model Predictive Control with Control Barrier Functions for Autonomous Surface Vessels. <i>IEEE International Conference on Robotics and Automation </i>: 6089-6095. <a href=\"https://dx.doi.org/10.1109/ICRA57147.2024.10610620\" target=\"_blank\">https://dx.doi.org/10.1109/ICRA57147.2024.10610620</a>","BEntID":447152,"PublicFlag":1,"CheckedFlag":1,"wosflag":null,"vabbflag":null,"RefStringPartII":". <i>IEEE International Conference on Robotics and Automation </i>: 6089-6095. <a href=\"https://dx.doi.org/10.1109/ICRA57147.2024.10610620\" target=\"_blank\">https://dx.doi.org/10.1109/ICRA57147.2024.10610620</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Wang, W.; Xiao, W.; Gonzalez-Garcia, A.; Swevers, J.; Ratti, C.; Rus, D.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Wang, W. <i>et al.</i>","Englishabstract":"In autonomous robot navigation, the trajectories from path planners are considered to be safe regions, and deviations could endanger vessels. Model Predictive Control (MPC) stands as a popular choice for trajectory tracking problems as it naturally addresses operational constraints, such as dynamics and control constraints. Nevertheless, achieving robustness in changing environments like oceans and rivers, which are constantly subject to significant external disturbances, remains an ongoing challenge for MPC. It must consistently keep the system within a predefined safe region (such as a reference trajectory) even in the presence of model inaccuracies and perturbations. To address this challenge, we present a robust model predictive control strategy utilizing Control Barrier Functions (CBFs), which increases the disturbance-rejection abilities. We verify our method on an autonomous surface vessel in simulation and natural waters, both with external disturbances. Specifically, compared with the traditional MPC method, our proposed MPC-CBF strategy reduces tracking errors by 17.82% and 40.26% in simulations and field experiments, respectively. Although the control effort slightly increases by 7.78% and 4.20%, respectively, these results clearly demonstrate the enhanced resilience of MPC-CBF to disturbances.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Robust Model Predictive Control with Control Barrier Functions for Autonomous Surface Vessels","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-10-05 01:50:03.718606","timezone_type":1,"timezone":"+02:00"},"AuthorKeywords":"systems","OtherDescriptors":null,"Notes":null,"AnaPub":2024,"MonPub":null,"DateUpdate":"2026-09-28","DateCreate":"2026-09-28","SecASFANote":null,"ConfID":null,"PeerRev":0,"VlizCoreFlag":1,"WoScode":"WOS:001294576204098","VABBcode":null,"OpenAcc":0,"DOI":"10.1109/ICRA57147.2024.10610620"},"refs":null,"anarec":{"AnaID":449314,"PubliDate":2024,"Pagination":"6089-6095","XtraPublOfAnaID":null,"ISBN":null,"Volume":null,"Issue":null,"BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":448341,"SerRR":"IEEE International Conference on Robotics and Automation. 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