In practical applications, an Unmanned Aerial System's (UAS) baseline performance is dictated by how well it can follow a given trajectory with limited stress on the actuators. However, these can be insufficient performance metrics when the UAS is allowed to adapt to an unpredicted external influence such as turbulence or actuation failure, while maintaining a satisfactory baseline performance.
In this thesis, different control laws based on the formation flight geometry problem, nonlinear dynamic inversion and an artificial immune system adaptive mechanism , are implemented in hardware-in-the-loop as a precursor for in-flight testing. These controllers are compared based on three performance metrics: trajectory following, control activity and computer task execution time. The controllers chosen for comparison are: Basic Proportional-Integral-Derivative (PID), Outer loop Non-Linear Dynamic Inversion (NLDI), Extended NLDI, and the previous three controllers augmented with an AIS for a total of six controllers. The Extended NLDI augmented with the AIS outperformed all of the other algorithms under failure conditions on a global scale.
","abstract_html":"<p>In practical applications, an Unmanned Aerial System's (UAS) baseline performance is dictated by how well it can follow a given trajectory with limited stress on the actuators. However, these can be insufficient performance metrics when the UAS is allowed to adapt to an unpredicted external influence such as turbulence or actuation failure, while maintaining a satisfactory baseline performance.</p> <p>In this thesis, different control laws based on the formation flight geometry problem, nonlinear dynamic inversion and an artificial immune system adaptive mechanism , are implemented in hardware-in-the-loop as a precursor for in-flight testing. These controllers are compared based on three performance metrics: trajectory following, control activity and computer task execution time. The controllers chosen for comparison are: Basic Proportional-Integral-Derivative (PID), Outer loop Non-Linear Dynamic Inversion (NLDI), Extended NLDI, and the previous three controllers augmented with an AIS for a total of six controllers. The Extended NLDI augmented with the AIS outperformed all of the other algorithms under failure conditions on a global scale.</p>","abstract_has_math":false,"creators":["Lyons, Brendon"],"institution":null,"degree_name":"Master of Science in Mechanical Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-11-01T07:00:00Z","date_published":"2013-11-01T07:00:00Z","updated_at":"2026-07-27T19:26:08Z","subjects":["adaptive control","non-linear","hardware","unmanned aerial systems","Mechanical Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/98","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Lyons, Brendon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Mechanical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["adaptive control","non-linear","hardware","unmanned aerial systems","Mechanical Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/98"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In practical applications, an Unmanned Aerial System's (UAS) baseline performance is dictated by how well it can follow a given trajectory with limited stress on the actuators. However, these can be insufficient performance metrics when the UAS is allowed to adapt to an unpredicted external influence such as turbulence or actuation failure, while maintaining a satisfactory baseline performance.
In this thesis, different control laws based on the formation flight geometry problem, nonlinear dynamic inversion and an artificial immune system adaptive mechanism , are implemented in hardware-in-the-loop as a precursor for in-flight testing. These controllers are compared based on three performance metrics: trajectory following, control activity and computer task execution time. The controllers chosen for comparison are: Basic Proportional-Integral-Derivative (PID), Outer loop Non-Linear Dynamic Inversion (NLDI), Extended NLDI, and the previous three controllers augmented with an AIS for a total of six controllers. The Extended NLDI augmented with the AIS outperformed all of the other algorithms under failure conditions on a global scale.
"]},{"key":"dc:title","label":"Title","values":["Performance Analysis Of Non-Linear Adaptive Control Laws Using Hardware in the Loop of an Unmanned Aerial System"]}]}],"canonical_facts":{"dc:creator":["Lyons, Brendon"],"dc:description.abstract":["In practical applications, an Unmanned Aerial System's (UAS) baseline performance is dictated by how well it can follow a given trajectory with limited stress on the actuators. However, these can be insufficient performance metrics when the UAS is allowed to adapt to an unpredicted external influence such as turbulence or actuation failure, while maintaining a satisfactory baseline performance.
In this thesis, different control laws based on the formation flight geometry problem, nonlinear dynamic inversion and an artificial immune system adaptive mechanism , are implemented in hardware-in-the-loop as a precursor for in-flight testing. These controllers are compared based on three performance metrics: trajectory following, control activity and computer task execution time. The controllers chosen for comparison are: Basic Proportional-Integral-Derivative (PID), Outer loop Non-Linear Dynamic Inversion (NLDI), Extended NLDI, and the previous three controllers augmented with an AIS for a total of six controllers. The Extended NLDI augmented with the AIS outperformed all of the other algorithms under failure conditions on a global scale.
"],"dc:identifier":["https://commons.erau.edu/edt/98"],"dc:subject":["adaptive control","non-linear","hardware","unmanned aerial systems","Mechanical Engineering"],"dc:title":["Performance Analysis Of Non-Linear Adaptive Control Laws Using Hardware in the Loop of an Unmanned Aerial System"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Mechanical Engineering"]},"updated_at":"2026-07-27T19:26:08Z"}