Design of Reinforcement Learning based Incremental Flight Control Laws for the Cessna Citation II(PH-LAB) Aircraft

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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.