Robust Reinforcement Learning for Flight Control
Model-free fault-tolerant flight control
J.B. Pinto de Moura Leite da Cunha (TU Delft - Aerospace Engineering)
E. van Kampen – Mentor (TU Delft - Aerospace Engineering)
Spilios Theodoulis – Mentor (TU Delft - Aerospace Engineering)
M.D. Pavel – Graduation committee member (TU Delft - Aerospace Engineering)
E. Mooij – Graduation committee member (TU Delft - Aerospace Engineering)
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Abstract
H∞ robust control is a powerful tool to design controllers robust against disturbances and model uncertainties. However, when a fault occurs in the system, H∞ controllers typically have reduced adaptability to the fault, possibly requiring costly system identification steps to adapt the controller to the unknown faulty model. In this research, integral reinforcement learning (IRL) is applied to the H∞ tracking control problem to adapt a controller online without any knowledge of system dynamics, where only system trajectory data is available to the learning algorithm. Both off-policy and on-policy IRL methods are applied to a pitch-rate tracker for the linearised F-16 short-period dynamics after a fault of 50% reduction in elevator actuator effectiveness has occurred. The adapted controller converges to the model-based controller for the faulty system within 15 s, and its tracking performance is compared to that of the controller for the nominal system.