Multi-agent systems have become a powerful tool for a wide range of applications due to the effective and cheap solutions they offer. Especially during the last decade, they have impacted a wide array of civilian and military applications (e.g., surveillance, reconnaissance, and payload transportation). However, they suffer system anomalies due to the operational and material conditions, which yields degraded performance or even instability. Hence, this dissertation investigates the distributed adaptive control design process for uncertain multi-agent systems with scalar and high-order dynamics in the presence of unknown control effectiveness and actuator dynamics. First, it provides an approach for driving a set of agents to user-assigned positions for missions that require agents to operate in formations. Then, it introduces a command governor mechanism for multi-agent systems with scalar dynamics for utilizing low learning rates on agent-based uncertainty and unknown control effectiveness estimations while ensuring/improving the transient response of the overall multi-agent system. It analyzes the stability of the closed-loop multi-agent systems that use the distributed adaptive controllers and hedging-based reference model design with and without the additional command governor mechanism, by using the Lyapunov Stability Theory and Linear Matrix Inequalities (LMI). In addition, the boundedness of the reference model and actuator dynamics is investigated via the LMI technique, and a feasible actuator bandwidth set is shown. Through the simulation and experimentation results, it is shown that the scalar dynamics assumption on real-time physical systems yields oscillatory transient performance. Hence, this dissertation modifies the reference model with scalar dynamics by extending the reference model dynamics to include higher-order dynamics, which decreases the mismatch between the actual system and the reference model dynamics. It also develops a command governor mechanism for multi-agent systems with high-order dynamics to provide satisfactory transient performance while using low learning rates. Finally, it illustrates the efficacy of the proposed distributed adaptive controllers based on high-order reference model dynamics and command governor mechanism through simulation and experimentation results.
","abstract_html":"<p>Multi-agent systems have become a powerful tool for a wide range of applications due to the effective and cheap solutions they offer. Especially during the last decade, they have impacted a wide array of civilian and military applications (e.g., surveillance, reconnaissance, and payload transportation). However, they suffer system anomalies due to the operational and material conditions, which yields degraded performance or even instability. Hence, this dissertation investigates the distributed adaptive control design process for uncertain multi-agent systems with scalar and high-order dynamics in the presence of unknown control effectiveness and actuator dynamics. First, it provides an approach for driving a set of agents to user-assigned positions for missions that require agents to operate in formations. Then, it introduces a command governor mechanism for multi-agent systems with scalar dynamics for utilizing low learning rates on agent-based uncertainty and unknown control effectiveness estimations while ensuring/improving the transient response of the overall multi-agent system. It analyzes the stability of the closed-loop multi-agent systems that use the distributed adaptive controllers and hedging-based reference model design with and without the additional command governor mechanism, by using the Lyapunov Stability Theory and Linear Matrix Inequalities (LMI). In addition, the boundedness of the reference model and actuator dynamics is investigated via the LMI technique, and a feasible actuator bandwidth set is shown. Through the simulation and experimentation results, it is shown that the scalar dynamics assumption on real-time physical systems yields oscillatory transient performance. Hence, this dissertation modifies the reference model with scalar dynamics by extending the reference model dynamics to include higher-order dynamics, which decreases the mismatch between the actual system and the reference model dynamics. It also develops a command governor mechanism for multi-agent systems with high-order dynamics to provide satisfactory transient performance while using low learning rates. Finally, it illustrates the efficacy of the proposed distributed adaptive controllers based on high-order reference model dynamics and command governor mechanism through simulation and experimentation results.</p>","abstract_has_math":false,"creators":["Kurttisi, Atahan"],"institution":null,"degree_name":"Doctor of Philosophy in Aerospace Engineering","degree_level":"Dissertation - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-04-01T07:00:00Z","date_published":"2024-04-01T07:00:00Z","updated_at":"2026-07-27T19:25:52Z","subjects":["distributed adaptive control","multi-agent systems","command governor mechanism","model reference adaptive control","leader-follower control","Controls and Control Theory","Navigation, Guidance, Control and Dynamics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/787","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Kurttisi, Atahan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2026-05-01T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["distributed adaptive control","multi-agent systems","command governor mechanism","model reference adaptive control","leader-follower control","Controls and Control Theory","Navigation, Guidance, Control and Dynamics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/787"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Multi-agent systems have become a powerful tool for a wide range of applications due to the effective and cheap solutions they offer. Especially during the last decade, they have impacted a wide array of civilian and military applications (e.g., surveillance, reconnaissance, and payload transportation). However, they suffer system anomalies due to the operational and material conditions, which yields degraded performance or even instability. Hence, this dissertation investigates the distributed adaptive control design process for uncertain multi-agent systems with scalar and high-order dynamics in the presence of unknown control effectiveness and actuator dynamics. First, it provides an approach for driving a set of agents to user-assigned positions for missions that require agents to operate in formations. Then, it introduces a command governor mechanism for multi-agent systems with scalar dynamics for utilizing low learning rates on agent-based uncertainty and unknown control effectiveness estimations while ensuring/improving the transient response of the overall multi-agent system. It analyzes the stability of the closed-loop multi-agent systems that use the distributed adaptive controllers and hedging-based reference model design with and without the additional command governor mechanism, by using the Lyapunov Stability Theory and Linear Matrix Inequalities (LMI). In addition, the boundedness of the reference model and actuator dynamics is investigated via the LMI technique, and a feasible actuator bandwidth set is shown. Through the simulation and experimentation results, it is shown that the scalar dynamics assumption on real-time physical systems yields oscillatory transient performance. Hence, this dissertation modifies the reference model with scalar dynamics by extending the reference model dynamics to include higher-order dynamics, which decreases the mismatch between the actual system and the reference model dynamics. It also develops a command governor mechanism for multi-agent systems with high-order dynamics to provide satisfactory transient performance while using low learning rates. Finally, it illustrates the efficacy of the proposed distributed adaptive controllers based on high-order reference model dynamics and command governor mechanism through simulation and experimentation results.
"]},{"key":"dc:title","label":"Title","values":["Trustable Adaptive Controllers for Multi-Agent Systems with Actuator Dynamics"]}]}],"canonical_facts":{"dc:creator":["Kurttisi, Atahan"],"dc:date.available":["2026-05-01T07:00:00Z"],"dc:description.abstract":["Multi-agent systems have become a powerful tool for a wide range of applications due to the effective and cheap solutions they offer. Especially during the last decade, they have impacted a wide array of civilian and military applications (e.g., surveillance, reconnaissance, and payload transportation). However, they suffer system anomalies due to the operational and material conditions, which yields degraded performance or even instability. Hence, this dissertation investigates the distributed adaptive control design process for uncertain multi-agent systems with scalar and high-order dynamics in the presence of unknown control effectiveness and actuator dynamics. First, it provides an approach for driving a set of agents to user-assigned positions for missions that require agents to operate in formations. Then, it introduces a command governor mechanism for multi-agent systems with scalar dynamics for utilizing low learning rates on agent-based uncertainty and unknown control effectiveness estimations while ensuring/improving the transient response of the overall multi-agent system. It analyzes the stability of the closed-loop multi-agent systems that use the distributed adaptive controllers and hedging-based reference model design with and without the additional command governor mechanism, by using the Lyapunov Stability Theory and Linear Matrix Inequalities (LMI). In addition, the boundedness of the reference model and actuator dynamics is investigated via the LMI technique, and a feasible actuator bandwidth set is shown. Through the simulation and experimentation results, it is shown that the scalar dynamics assumption on real-time physical systems yields oscillatory transient performance. Hence, this dissertation modifies the reference model with scalar dynamics by extending the reference model dynamics to include higher-order dynamics, which decreases the mismatch between the actual system and the reference model dynamics. It also develops a command governor mechanism for multi-agent systems with high-order dynamics to provide satisfactory transient performance while using low learning rates. Finally, it illustrates the efficacy of the proposed distributed adaptive controllers based on high-order reference model dynamics and command governor mechanism through simulation and experimentation results.
"],"dc:identifier":["https://commons.erau.edu/edt/787"],"dc:subject":["distributed adaptive control","multi-agent systems","command governor mechanism","model reference adaptive control","leader-follower control","Controls and Control Theory","Navigation, Guidance, Control and Dynamics"],"dc:title":["Trustable Adaptive Controllers for Multi-Agent Systems with Actuator Dynamics"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Dissertation - Open Access"],"thesis:degree_name":["Doctor of Philosophy in Aerospace Engineering"]},"updated_at":"2026-07-27T19:25:52Z"}