Reinforcement Learning for Flight Control: Evaluating Handling Qualities and Stability Properties of the PH-LAB

Reinforcement Learning applied to flight control has shown to have several benefits over classical, linear flight controllers, as it eliminates the need for gain scheduling and it could provide fault-tolerance. The application to civil aviation in practice, however, is non-existent as there are multiple safety concerns. This research...

Explainable Reinforcement Learning in Flight Control through Reward Decomposition

Even though Deep Reinforcement Learning (DRL) techniques have proven their ability to solve highly complex control tasks, the opaqueness and inexplicability associated with these solutions many times stops them from being applied to real flight control applications. In this research, reward decomposition explanations are used to tackle this...

Deep Reinforcement Learning for Flight Control: Fault-Tolerant Control for the PH-LAB

Fault-tolerant flight control faces challenges as developing a model-based controller for each unexpected failure is unrealistic, and online learning methods can handle limited system complexity due to their low sample efficiency. In this research, a model-free coupled-dynamics flight controller for a jet aircraft able to withstand multiple...

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

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...

Online Reinforcement Learning for Flight Control: An Adaptive Critic Design without prior model knowledge

Online Reinforcement Learning is a possible solution for adaptive nonlinear flight control. In this research an Adaptive Critic Design (ACD) based on Dual Heuristic Dynamic Programming (DHP) is developed and implemented on a simulated Cessna Citation 550 aircraft. Using an online identified system model approximation, the method is independent...

Nonlinear Control Allocation for a High-Performance Tailless Aircraft with Innovative Control Effectors: An Incremental Robust Approach

Conventional linear control allocation (LCA) methods fail to provide satisfactory performance in flight control systems (FCS) for aircraft with highly nonlinear and coupled control effector suites, especially for tailless aircraft with strong interactions between control effectors. This thesis implements an incremental nonlinear control...