The autonomous flight industry is ever-expanding and continuing to push the boundaries of what is possible within the limitations of technology. Multiple companies are exploring the use of autonomous flight for intra-city travel with air taxi services and package delivery vehicles. Other companies are exploring the use of autonomous vehicles for military applications, such as Sikorsky with a next generation Black Hawk helicopter to ensure the safety of soldiers in high threat or altogether dangerous scenarios. In this thesis model predictive control (MPC) algorithms are developed to enable a quadcopter to operate and land autonomously in challenging environments. Specifically, MPC entails repeatedly solving an optimal control problem over a given time horizon. Constraints may be applied based on the vehicle dynamics, obstacle avoidance, and a desired terminal state such as a landing zone. The MPC algorithm developed includes an optimization method known as interior point optimization (IPOPT) and utilizes an objective function built from preceding linear quadratic regulator (LQR) cost functions. This objective function was constructed in a similar fashion to that of the LQR cost function but includes augmentations for the optimization, to handle constraints and obstacles, and to ensure stability of the MPC algorithm. Scenarios considered in this research include autonomous operations and landing in an obstacle environment, and recovery operations such as landing in the event of a failure or power loss. The MPC algorithms will be implemented and evaluated in simulation studies for these representative scenarios.
","abstract_html":"<p>The autonomous flight industry is ever-expanding and continuing to push the boundaries of what is possible within the limitations of technology. Multiple companies are exploring the use of autonomous flight for intra-city travel with air taxi services and package delivery vehicles. Other companies are exploring the use of autonomous vehicles for military applications, such as Sikorsky with a next generation Black Hawk helicopter to ensure the safety of soldiers in high threat or altogether dangerous scenarios. In this thesis model predictive control (MPC) algorithms are developed to enable a quadcopter to operate and land autonomously in challenging environments. Specifically, MPC entails repeatedly solving an optimal control problem over a given time horizon. Constraints may be applied based on the vehicle dynamics, obstacle avoidance, and a desired terminal state such as a landing zone. The MPC algorithm developed includes an optimization method known as interior point optimization (IPOPT) and utilizes an objective function built from preceding linear quadratic regulator (LQR) cost functions. This objective function was constructed in a similar fashion to that of the LQR cost function but includes augmentations for the optimization, to handle constraints and obstacles, and to ensure stability of the MPC algorithm. Scenarios considered in this research include autonomous operations and landing in an obstacle environment, and recovery operations such as landing in the event of a failure or power loss. The MPC algorithms will be implemented and evaluated in simulation studies for these representative scenarios.</p>","abstract_has_math":false,"creators":["Sotirakos, Konstantinos"],"institution":null,"degree_name":"Master of Aerospace Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-10-01T07:00:00Z","date_published":"2024-10-01T07:00:00Z","updated_at":"2026-07-27T19:26:16Z","subjects":["Model Predictive Control","Autonomy","Navigation, Guidance, Control and Dynamics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/863","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Sotirakos, Konstantinos"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Model Predictive Control","Autonomy","Navigation, Guidance, Control and Dynamics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/863"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The autonomous flight industry is ever-expanding and continuing to push the boundaries of what is possible within the limitations of technology. Multiple companies are exploring the use of autonomous flight for intra-city travel with air taxi services and package delivery vehicles. Other companies are exploring the use of autonomous vehicles for military applications, such as Sikorsky with a next generation Black Hawk helicopter to ensure the safety of soldiers in high threat or altogether dangerous scenarios. In this thesis model predictive control (MPC) algorithms are developed to enable a quadcopter to operate and land autonomously in challenging environments. Specifically, MPC entails repeatedly solving an optimal control problem over a given time horizon. Constraints may be applied based on the vehicle dynamics, obstacle avoidance, and a desired terminal state such as a landing zone. The MPC algorithm developed includes an optimization method known as interior point optimization (IPOPT) and utilizes an objective function built from preceding linear quadratic regulator (LQR) cost functions. This objective function was constructed in a similar fashion to that of the LQR cost function but includes augmentations for the optimization, to handle constraints and obstacles, and to ensure stability of the MPC algorithm. Scenarios considered in this research include autonomous operations and landing in an obstacle environment, and recovery operations such as landing in the event of a failure or power loss. The MPC algorithms will be implemented and evaluated in simulation studies for these representative scenarios.
"]},{"key":"dc:title","label":"Title","values":["Model Predictive Control for Autonomous Landing in Complex Scenarios"]}]}],"canonical_facts":{"dc:creator":["Sotirakos, Konstantinos"],"dc:description.abstract":["The autonomous flight industry is ever-expanding and continuing to push the boundaries of what is possible within the limitations of technology. Multiple companies are exploring the use of autonomous flight for intra-city travel with air taxi services and package delivery vehicles. Other companies are exploring the use of autonomous vehicles for military applications, such as Sikorsky with a next generation Black Hawk helicopter to ensure the safety of soldiers in high threat or altogether dangerous scenarios. In this thesis model predictive control (MPC) algorithms are developed to enable a quadcopter to operate and land autonomously in challenging environments. Specifically, MPC entails repeatedly solving an optimal control problem over a given time horizon. Constraints may be applied based on the vehicle dynamics, obstacle avoidance, and a desired terminal state such as a landing zone. The MPC algorithm developed includes an optimization method known as interior point optimization (IPOPT) and utilizes an objective function built from preceding linear quadratic regulator (LQR) cost functions. This objective function was constructed in a similar fashion to that of the LQR cost function but includes augmentations for the optimization, to handle constraints and obstacles, and to ensure stability of the MPC algorithm. Scenarios considered in this research include autonomous operations and landing in an obstacle environment, and recovery operations such as landing in the event of a failure or power loss. The MPC algorithms will be implemented and evaluated in simulation studies for these representative scenarios.
"],"dc:identifier":["https://commons.erau.edu/edt/863"],"dc:subject":["Model Predictive Control","Autonomy","Navigation, Guidance, Control and Dynamics"],"dc:title":["Model Predictive Control for Autonomous Landing in Complex Scenarios"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Aerospace Engineering"]},"updated_at":"2026-07-27T19:26:16Z"}