{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/87096"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/87096","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Control and Estimation Algorithms for Multiple-Agent Systems","abstract":"Motivated by the applications to the optimal mobile sensor positioning within mobile sensor networks, the perturbation-based extremum seeking algorithm has been modified and extended. It has been assumed that the integrator gain and the perturbation amplitude are time varying (decreasing in time with a proper rate) and that the output is corrupted with measurement noise. The proposed basic, one dimensional, algorithm has been extended to two dimensional, hybrid schemes and directly applied to the planar optimal mobile sensor positioning, where the vehicles can be modeled as velocity actuated point masses, force actuated point masses, or nonholonomic unicycles. The convergence of all the proposed algorithms, with probability one and in the mean square sense, has been proved. Also, the problem of target assignment in multi-agent systems using multi-variable extremum seeking algorithm has been addressed. An algorithm which effectively solves the problem has been proposed, based on the local extremum seeking of the specially designed global utility functions which capture the dependance among different, possibly conflicting objectives of the agents. It has been demonstrated how the utility function parameters and agents' initial conditions impact the trajectories and destinations of the agents. All the proposed extremum seeking based algorithms have been illustrated with several simulations.","abstract_html":"Motivated by the applications to the optimal mobile sensor positioning within mobile sensor networks, the perturbation-based extremum seeking algorithm has been modified and extended. It has been assumed that the integrator gain and the perturbation amplitude are time varying (decreasing in time with a proper rate) and that the output is corrupted with measurement noise. The proposed basic, one dimensional, algorithm has been extended to two dimensional, hybrid schemes and directly applied to the planar optimal mobile sensor positioning, where the vehicles can be modeled as velocity actuated point masses, force actuated point masses, or nonholonomic unicycles. The convergence of all the proposed algorithms, with probability one and in the mean square sense, has been proved. Also, the problem of target assignment in multi-agent systems using multi-variable extremum seeking algorithm has been addressed. An algorithm which effectively solves the problem has been proposed, based on the local extremum seeking of the specially designed global utility functions which capture the dependance among different, possibly conflicting objectives of the agents. It has been demonstrated how the utility function parameters and agents' initial conditions impact the trajectories and destinations of the agents. All the proposed extremum seeking based algorithms have been illustrated with several simulations.","abstract_has_math":false,"creators":["Stankovic, Milos"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Systems and Entrepreneurial Engineering","degree_department":null,"school":null,"contributors":["Dusan Stipanovic","Sreenivas, Ramavarapu S."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-28T15:37:24Z","date_published":"2015-09-28T15:37:24Z","updated_at":"2026-07-22T22:26:28Z","subjects":["Engineering, Mechanical"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3392483"],"render_values":[{"text":"(MiAaPQ)AAI3392483","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/87096","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dusan Stipanovic","Sreenivas, Ramavarapu S."]},{"key":"dc:creator","label":"Author","values":["Stankovic, Milos"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-28T15:37:24Z","10000-01-01","2009"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Systems and Entrepreneurial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering, Mechanical"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/87096","(MiAaPQ)AAI3392483"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Motivated by the applications to the optimal mobile sensor positioning within mobile sensor networks, the perturbation-based extremum seeking algorithm has been modified and extended. It has been assumed that the integrator gain and the perturbation amplitude are time varying (decreasing in time with a proper rate) and that the output is corrupted with measurement noise. The proposed basic, one dimensional, algorithm has been extended to two dimensional, hybrid schemes and directly applied to the planar optimal mobile sensor positioning, where the vehicles can be modeled as velocity actuated point masses, force actuated point masses, or nonholonomic unicycles. The convergence of all the proposed algorithms, with probability one and in the mean square sense, has been proved. Also, the problem of target assignment in multi-agent systems using multi-variable extremum seeking algorithm has been addressed. An algorithm which effectively solves the problem has been proposed, based on the local extremum seeking of the specially designed global utility functions which capture the dependance among different, possibly conflicting objectives of the agents. It has been demonstrated how the utility function parameters and agents' initial conditions impact the trajectories and destinations of the agents. All the proposed extremum seeking based algorithms have been illustrated with several simulations.","Made available in DSpace on 2015-09-28T15:37:24Z (GMT). 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It has been assumed that the integrator gain and the perturbation amplitude are time varying (decreasing in time with a proper rate) and that the output is corrupted with measurement noise. The proposed basic, one dimensional, algorithm has been extended to two dimensional, hybrid schemes and directly applied to the planar optimal mobile sensor positioning, where the vehicles can be modeled as velocity actuated point masses, force actuated point masses, or nonholonomic unicycles. The convergence of all the proposed algorithms, with probability one and in the mean square sense, has been proved. Also, the problem of target assignment in multi-agent systems using multi-variable extremum seeking algorithm has been addressed. An algorithm which effectively solves the problem has been proposed, based on the local extremum seeking of the specially designed global utility functions which capture the dependance among different, possibly conflicting objectives of the agents. 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