Model-free control algorithms for micro air vehicles with transitioning flight capabilities

Abstract

Micro air vehicles with transitioning flight capabilities, or simply hybrid micro air vehicles, combine the beneficial features of fixed-wing configurations, in terms of endurance, with vertical takeoff and landing capabilities of rotorcrafts to perform five different flight phases during typical missions, such as vertical takeoff, transitioning flight, forward flight, hovering and vertical landing. This promising micro air vehicle class has a wider flight envelope than conventional micro air vehicles, which implies new challenges for both control community and aerodynamic designers. One of the major challenges of hybrid micro air vehicles is the fast variation of aerodynamic forces and moments during the transition flight phase which is difficult to model accurately. To overcome this problem, we propose a flight control architecture that estimates and counteracts in real-time these fast dynamics with an intelligent feedback controller. The proposed flight controller is designed to stabilize the hybrid micro air vehicle attitude as well as its velocity and position during all flight phases. By using model-free control algorithms, the proposed flight control architecture bypasses the need for a precise hybrid micro air vehicle model that is costly and time consuming to obtain. A comprehensive set of flight simulations covering the entire flight envelope of tailsitter micro air vehicles is presented. Finally, real-world flight tests were conducted to compare the model-free control performance to that of the Incremental Nonlinear Dynamic Inversion controller, which has been applied to a variety of aircraft providing effective flight performances.

Project of high altitude balloon launched micro glider: Aircraft design, control and flight test

“…Wind direction points to the waypoint. Flight results in the wind fields are shown in Figures 19 and 20. As can be seen, tailwind results show a significant reduction to the flight range.…”

“…The elevons separated the aerodynamic model may be more accurate for flat-plate flying wing MAVs. 20 Secondly, the accuracy of fabrication needs to be enhanced. Effectiveness for the increase of δ a, limited will be validated in the further launch tests.…”

Machine Learning and Control Engineering: The Model-Free Case

“…It implies that the tuning of K P is quite straightforward. 8 This is a major benefit when compared to the tuning of "classic" PIDs (see, e.g., [4,5]).…”

Robust optimal transition maneuvers control for tail‐sitter unmanned aerial vehicles

“…So far, lots of investigations on the transition trajectory and controller design have been conducted for different types of VTOL UAVs including the tail‐sitter 14‐16 . In the work of Kubo et al, 17 linear quadric regulator (LQR) was employed by combining an offline trained neural network as a transition reference command generator.…”