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dc.contributor.authorGuler, Samet
dc.contributor.authorFidan, Baris
dc.contributor.authorDasgupta, Soura
dc.contributor.authorAnderson, Brian D.O.
dc.contributor.authorShames, Iman
dc.date.accessioned2017-05-25T10:55:13Z
dc.date.available2017-05-25T10:55:13Z
dc.date.issued2016-10-26
dc.identifier.citationGuler S, Fidan B, Dasgupta S, Anderson BDO, Shames I (2016) Adaptive Source Localization Based Station Keeping of Autonomous Vehicles. IEEE Transactions on Automatic Control: 1–1. Available: http://dx.doi.org/10.1109/tac.2016.2621764.
dc.identifier.issn0018-9286
dc.identifier.issn1558-2523
dc.identifier.doi10.1109/tac.2016.2621764
dc.identifier.urihttp://hdl.handle.net/10754/623707
dc.description.abstractWe study the problem of driving a mobile sensory agent to a target whose location is specied only in terms of the distances to a set of sensor stations or beacons. The beacon positions are unknown, but the agent can continuously measure its distances to them as well as its own position. This problem has two particular applications: (1) capturing a target signal source whose distances to the beacons are measured by these beacons and broadcasted to a surveillance agent, (2) merging a single agent to an autonomous multi-agent system so that the new agent is positioned at desired distances from the existing agents. The problem is solved using an adaptive control framework integrating a parameter estimator producing beacon location estimates, and an adaptive motion control law fed by these estimates to steer the agent toward the target. For location estimation, a least-squares adaptive law is used. The motion control law aims to minimize a convex cost function with unique minimizer at the target location, and is further augmented for persistence of excitation. Stability and convergence analysis is provided, as well as simulation results demonstrating performance and transient behavior.
dc.description.sponsorshipThis work is supported by the Canadian NSERC Discovery Grant 116806. This work is supported in part by US NSF grants EPS-1101284, CNS-1329657, CCF-1302456, ONR grant N00014-13-1-0202 and under the Thousand Talents Program of the State and Shandong Province, administered by the Shandong Academy of Sciences, China.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/7707439/
dc.rights(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectOptimization
dc.subjectAdaptive Control
dc.subjectAutonomous Robots
dc.subjectCooperative Control
dc.subjectSensor Networks
dc.titleAdaptive Source Localization Based Station Keeping of Autonomous Vehicles
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalIEEE Transactions on Automatic Control
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, N2L 3G1 Canada
dc.contributor.institutionDepartment of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, 52242 USA
dc.contributor.institutionShandong Computer Science Center (National Supercomputer Center in Jinan), Shandong Provincial Key Laboratory of Computer Networks
dc.contributor.institutionResearch School of Information Sciences and Engineering, Australian National University, Canberra, ACT, 0200 Australia
dc.contributor.institutionNational ICT Australia (NICTA), NSW 2601, Canberra, Australia
dc.contributor.institutionDepartment of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC 3010, Australia
kaust.personGuler, Samet
refterms.dateFOA2018-06-13T17:24:40Z
dc.date.published-online2016-10-26
dc.date.published-print2017-07


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