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...

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...

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 the Automatic Six-Degree-of-Freedom Docking Maneuver of Space Vehicles

The implementation of a model-free, off-policy, actor-critic deep reinforcement learning algorithm consistent of two separate agents to a six-degree-of freedom spacecraft docking maneuver to develop a control policy is carried out in the research presented in this article. Reinforcement learning has the ability to learn without instruction, this...

Distributional Reinforcement Learning for Flight Control: A risk-sensitive approach to aircraft attitude control using Distributional RL

With the recent increase in the complexity of aerospace systems and autonomous operations, there is a need for an increased level of adaptability and model-free controller synthesis. Such operations require the controller to maintain safety and performance without human intervention in non-static environments with partial observability and...

Evolutionary Reinforcement Learning: A Hybrid Approach for Safety-informed Intelligent Fault-tolerant Flight Control

Recent research in bio-inspired artificial intelligence potentially provides solutions to the challenging problem of designing fault-tolerant and robust flight control systems. The current work proposes SERL, a novel Safety-informed Evolutionary Reinforcement Learning algorithm, which combines Deep Reinforcement Learning (DRL) and neuro...

Deep Reinforcement Learning for Aircraft Landing: A study on the use of Deep Reinforcement Learning techniques for automatic control of aircraft landing

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...