A Novel Hybrid Approach for Fault-Tolerant Control of UAVs based on Robust Reinforcement Learning

The control of complex autonomous systems has significantly improved in recent years and unmanned aerial vehicles (UAVs) have become popular in the research community. Although the use of UAVs is increasing, much work remains to guarantee fault- tolerant control (FTC) properties of these vehicles. Model-based controllers are the standard way to control UAVs, however obtaining models of the system and environment for every possible operating condition a UAV can experience in a real-world scenario is not feasible. Reinforcement Learning has shown promise in controlling complex systems but requires training in a simulator (requiring a model) of the system. Further, stability guarantees do not exist for learning-based controllers, which limits their large scale application in the real-world. We propose a novel hybrid FTC approach that uses a learned supervisory controller (together with low-level PID controllers) with key stability guarantees. We use a robust reinforcement learning approach to learn the supervisory control parameters and prove stability. We empirically validate our framework using trajectory-following experiments (in simulation) for a quadcopter subject to rotor faults, wind disturbances, and severe position and attitude noise.