Toward Nonlinear Flight Control for Fixed-Wing UAVs: System Architecture, Field Experiments, and Lessons Learned

Abstract

Inner-loop control algorithms in state-of-the-art autopilots for fixed-wing unmanned aerial vehicles (UAVs) are typically designed using linear control theory, to operate in relatively conservative flight envelopes. In the Autofly project, we seek to extend the flight envelopes of fixed-wing UAVs to allow for more aggressive maneuvering and operation in a wider range of weather conditions. Throughout the last few years, we have successfully flight tested several inner-loop attitude controllers for fixed-wing UAVs using advanced nonlinear control methods, including nonlinear model predictive control (NMPC), deep reinforcement learning (DRL), and geometric attitude control. To achieve this, we have developed a flexible embedded platform, capable of running computationally demanding low-level controllers that require direct actuator control. For safe operation and rapid development cycles, this platform can be deployed in tandem with well-tested standard autopilots. In this paper, we summarize the challenges and lessons learned, and document the system architecture of our experimental platform in a best-practice manner. This lowers the threshold for other researchers and engineers to employ new low-level control algorithms for fixed-wing UAVs. Case studies from outdoor field experiments are provided to demonstrate the efficacy of our research platform.