{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/66258"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/66258","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Self-Tuning Methods for Multiple-Controller Systems","abstract":"The optimization of stochastic systems with unknown parameters and multiple decision-makers or controllers each having his own objective is considered. Based on a centralized information pattern, steady-state solutions are obtained for the stochastic adaptive Nash game and Leader-Follower game problems. These adaptive solutions, after a judicious transformation, resemble closely the implicit self-tuning solution for the single-controller single-objective case, and thus preserve the salient and advantageous features of self-tuning methods--simplicity and easy implementation. In addition, due to this close resemblance, convergence results for the game problems are established by extending the convergence result from the single-controller single-objective case. The decentralized stochastic adaptive Nash game problem is also considered. Two explicit self-tuning type algorithms are proposed. The first algorithm is an ad hoc constraint on the policy form while the second one is based on extension from static Nash game theory. Simulation results indicate all these self-tuning methods are capable of stabilizing a system along targeted paths.","abstract_html":"The optimization of stochastic systems with unknown parameters and multiple decision-makers or controllers each having his own objective is considered. Based on a centralized information pattern, steady-state solutions are obtained for the stochastic adaptive Nash game and Leader-Follower game problems. These adaptive solutions, after a judicious transformation, resemble closely the implicit self-tuning solution for the single-controller single-objective case, and thus preserve the salient and advantageous features of self-tuning methods--simplicity and easy implementation. In addition, due to this close resemblance, convergence results for the game problems are established by extending the convergence result from the single-controller single-objective case. The decentralized stochastic adaptive Nash game problem is also considered. Two explicit self-tuning type algorithms are proposed. The first algorithm is an ad hoc constraint on the policy form while the second one is based on extension from static Nash game theory. Simulation results indicate all these self-tuning methods are capable of stabilizing a system along targeted paths.","abstract_has_math":false,"creators":["Chan, Yick Man"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-12T20:55:26Z","date_published":"2014-12-12T20:55:26Z","updated_at":"2026-07-22T22:25:55Z","subjects":["Engineering, Electronics and Electrical"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(UMI)AAI8203422"],"render_values":[{"text":"(UMI)AAI8203422","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/66258","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Chan, Yick Man"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-12-12T20:55:26Z","10000-01-01","1981"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical 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, Electronics and Electrical"]}]},{"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/2142/66258","(UMI)AAI8203422"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The optimization of stochastic systems with unknown parameters and multiple decision-makers or controllers each having his own objective is considered. Based on a centralized information pattern, steady-state solutions are obtained for the stochastic adaptive Nash game and Leader-Follower game problems. These adaptive solutions, after a judicious transformation, resemble closely the implicit self-tuning solution for the single-controller single-objective case, and thus preserve the salient and advantageous features of self-tuning methods--simplicity and easy implementation. In addition, due to this close resemblance, convergence results for the game problems are established by extending the convergence result from the single-controller single-objective case. The decentralized stochastic adaptive Nash game problem is also considered. Two explicit self-tuning type algorithms are proposed. The first algorithm is an ad hoc constraint on the policy form while the second one is based on extension from static Nash game theory. Simulation results indicate all these self-tuning methods are capable of stabilizing a system along targeted paths.","Made available in DSpace on 2014-12-12T20:55:26Z (GMT). No. of bitstreams: 1 8203422.pdf: 2786342 bytes, checksum: 7797be06e353a3182226539c25b695f0 (MD5) Previous issue date: 1981","Embargo set by: Seth Robbins for item 66437 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","110 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1981."]},{"key":"dc:title","label":"Title","values":["Self-Tuning Methods for Multiple-Controller Systems"]}]}],"canonical_facts":{"dc:creator":["Chan, Yick Man"],"dc:date":["2014-12-12T20:55:26Z","10000-01-01","1981"],"dc:description":["The optimization of stochastic systems with unknown parameters and multiple decision-makers or controllers each having his own objective is considered. Based on a centralized information pattern, steady-state solutions are obtained for the stochastic adaptive Nash game and Leader-Follower game problems. These adaptive solutions, after a judicious transformation, resemble closely the implicit self-tuning solution for the single-controller single-objective case, and thus preserve the salient and advantageous features of self-tuning methods--simplicity and easy implementation. In addition, due to this close resemblance, convergence results for the game problems are established by extending the convergence result from the single-controller single-objective case. The decentralized stochastic adaptive Nash game problem is also considered. Two explicit self-tuning type algorithms are proposed. The first algorithm is an ad hoc constraint on the policy form while the second one is based on extension from static Nash game theory. Simulation results indicate all these self-tuning methods are capable of stabilizing a system along targeted paths.","Made available in DSpace on 2014-12-12T20:55:26Z (GMT). No. of bitstreams: 1 8203422.pdf: 2786342 bytes, checksum: 7797be06e353a3182226539c25b695f0 (MD5) Previous issue date: 1981","Embargo set by: Seth Robbins for item 66437 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","110 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1981."],"dc:identifier":["http://hdl.handle.net/2142/66258","(UMI)AAI8203422"],"dc:language":["eng"],"dc:subject":["Engineering, Electronics and Electrical"],"dc:title":["Self-Tuning Methods for Multiple-Controller Systems"],"dc:type":["text"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:55Z"}