Print Email Facebook Twitter Design of Reinforcement Learning based Incremental Flight Control Laws for the Cessna Citation II(PH-LAB) Aircraft Title Design of Reinforcement Learning based Incremental Flight Control Laws for the Cessna Citation II(PH-LAB) Aircraft Author Konatala, Ramesh (TU Delft Aerospace Engineering; TU Delft Control & Simulation) Contributor van Kampen, E. (mentor) Looye, Gertjan H.N. (graduation committee) Chu, Q. P. (graduation committee) Mooij, E. (graduation committee) Sun, B. (graduation committee) Degree granting institution Delft University of Technology Programme Aerospace Engineering | Control & Simulation Date 2020-01-30 Abstract Online Adaptive Flight Control is interesting in the context of growing complexity of aircraft systems and their adaptability requirements to ensure safety. An Incremental Approximate Dynamic Programming (iADP) controller combines reinforcement learning methods, optimal control and Online identified incremental model to achieve optimal adaptive control suitable for Nonlinear Time-Varying systems. The main contribution of this thesis is twofold. Firstly, the iADP controller is designed to achieve automatic Online rate control to track pilot commands via setpoints provided by the manual outer loop on Citation II Aircraft model. Secondly, to assess the controller performance in the presence of sensor dynamics and actuator dynamics, an analysis is carried out to identify causes of any performance degradation. The simulation results from iADP longitudinal control using full state feedback indicate that the discretization of sensor signals, sensor bias and transport delays did not have any significant effect on the controller performance or on the incremental model identification. However noisy signals and sensors delays are found to cause controller performance degradation. Appropriate filtering of signals resulted in better estimation of the incremental model subsequently improving the controller performance due to noisy signals. Control performance degradation due to sensor delays should be addressed in future before conducting flight tests on Citation II Aircraft. Subject Reinforcement LearningIntelligent Flight ControlFlight Control SystemsMachine LearningAdaptive Flight ControlIncremental ControlIncremental Approximate Dynamic Programming To reference this document use: http://resolver.tudelft.nl/uuid:fb731f87-8124-4c6b-b756-1723a6cae80f Embargo date 2021-01-30 Part of collection Student theses Document type master thesis Rights © 2020 Ramesh Konatala Files PDF thesis_ramesh_konatala.pdf 6.42 MB Close viewer /islandora/object/uuid:fb731f87-8124-4c6b-b756-1723a6cae80f/datastream/OBJ/view