Electric vertical take-off and landing (eVTOL) aircraft enable new transport options in regional and urban air mobility. One promising but only little investigated and understood subcategory comprises tilt-wing eVTOLs. They offer high efficiency and long flight ranges but come with a trade-off in increased complexity. Consequently, a critical step towards market entry is the development of mature and safe hybrid pilot-autonomy control systems, including fault detection, identification, and recovery (FDIR) concepts. That requires a mid-fidelity dynamic model with sufficient accuracy, which is not yet available despite a long history of tilt-wing research. Without a representative model, no detailed analysis and identification of a trimmed transition trajectory could be performed. This, however, is a crucial step in the development of a control system. We approach the problem by applying and combining current modeling approaches. Furthermore, a trim analysis of different flight phases, including the transition, is conducted. The identified model lays the foundation for a representative and detailed development and investigation of future control designs, bringing tilt-wing eVTOLs closer to airworthiness.
Urban Air Mobility (UAM) promises an economic and ecological solution for the growing mobility demand by utilizing Electric Vertical Takeoff and Landing Vehicles (eVTOLs). Tilt-wing eVTOLs (e.g., Airbus A3 Vahana) appear to be the most promising ones because they offer an efficient wing-borne cruise flight while reducing the need for groundbased infrastructure at the cost of a complex control task. Tilt-wing vehicles increase the pilot's workload and introduce possible human and technical failures due to mechanical complexity. A unified control concept shall be able to handle the vehicle in every phase and provides a single clean and intuitive interface. This work develops a controller capable of decoupling the physical couplings of the flight dynamics. An integrated six-degree-of-freedom rigid body model in a compact mathematical representation is proposed, and flight control requirements are identified. An Incremental Nonlinear Dynamic Inversion (INDI) controller is designed which fulfills the requirements. Moreover, multiple command filters and outer-loop controllers are designed to handle different control modes and provide a proof-of-concept for a unified control scheme. Finally, the closed-loop system is evaluated by means of the control requirements and a generic UAM mission. The closed-loop system masters all parts of the mission and fulfills these requirements. The developed dynamic model and control system will be valuable for future tilt-wing eVTOL research, especially subsequent works on unified control systems.
To improve performance and efficiency of future aircraft generations, multi-objective control design problems considering effects such as over-actuation and lowly damped flexible modes arise from new materials in the wing design. A candidate method, which delivers a solution to this problem for the whole flight envelope is linear parameter varying (LPV) control synthesis. It already incorporates the controller scheduling in the synthesis process, guaranteeing stability and robustness over the entire scheduling envelope, and enables intuitive multi-objective, multiple-input multiple-output (MIMO) controller designs. To prove the concept of LPV controllers in a realistic environment, the flight test campaign results of LPV inner loop control laws on a Cessna Citation II (550) aircraft are presented in this paper. The implemented inner loop controllers are inspired by classical flight controllers used on state-of-the-art fly-by-wire airliners. The longitudinal motion is augmented with load-factor command and the lateral motion controller, which is inherently of MIMO type, features a roll rate command with attitude hold behavior. The control laws are validated in flight by the pilot with respect to functionality, flying and handling qualities. Furthermore, auto generated input signals are used to excite the aircraft without pilot in the loop to allow for a comparability with simulation results.
Reinforcement learning based methods could be feasible of solving adaptive optimal control problems for nonlinear dynamical systems. This work presents a proof of concept for applying reinforcement learning based methods to robust and adaptive flight control tasks. A framework for designing and examining these methods is introduced by means of the open research civil aircraft model (RCAM) and optimality criteria. A state-of-the-art robust flight controller -the incremental nonlinear dynamic inversion (INDI) controller -serves as a reference controller. Two intelligent control methods are introduced and examined. The deep deterministic policy gradient (DDPG) controller is selected as a promising actor critic reinforcement learning method that currently gains much attraction in the field of robotics. In addition, an adaptive version of a proportional-integral-derivative (PID) controller, the PID neural network (PIDNN) controller, is selected as the second method. The results show that all controllers are able to control the aircraft model. Moreover, the PIDNN controller exhibits improved reference tracking if a good initial guess of its weights is available. In turn, the DDPG algorithm is able to control the nonlinear aircraft model while minimizing a multi-objective value function. This work provides insight into the usability of selected intelligent controllers as flight control functions as well as a comparison to state-of-the-art flight control functions.
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