Reinforcement Learning for Flight Control: Hybrid Offline-Online Learning for Robust and Adaptive Fault-Tolerance

Recent advancements in fault-tolerant flight control have involved model-free offline and online Reinforcement Learning algorithms in order to provide robust and adaptive control to autonomous systems. Inspired by recent work on Incremental Dual Heuristic Programming (IDHP) and Soft Actor-Critic (SAC), this research proposes a hybrid SAC-IDHP...

Reinforcement Learning for Quadcopter Fault Tolerance: Analyzing Hierarchical and Online Deep Reinforcement Learning for Quadcopter Fault Tolerant Control

The demand of adding fault tolerance to quadcopter control systems has significantly increased with the rise of adoption of UAVs in numerous sectors. This work proposes and demonstrates the use of Hierarchical Reinforcement Learning to control a quadcopter subject to severe actuator fault. State-of-the-art algorithms are implemented, and a...

Adaptive Critic Control For Aircraft Lateral-Directional Dynamics: An Evaluation of J-SNAC Algorithm as a Fault Tolerant Flight Control Framework

Loss of control-in flight (LOC-I) is one of the causes of catastrophic aircraft accidents. Fault-tolerant flight control (FTFC) systems can prevent LOC-I and recover aircraft from its precursors. One group of promising methods for developing Fault-Tolerant Control (FTC) system is the Adaptive Critic Designs (ACD). Recently one ACD algorithm,...