Incremental nonlinear control allocation for an aircraft with distributed electric propulsion

Abstract

To meet the demanding requirements on the environmental impact of aircraft, radically new aircraft concepts need to be developed. Within the NOVAIR project, Royal Netherlands Aerospace Centre (NLR) tests these new concepts on a Scaled flight demonstrator (SFD). Using an SFD allows for testing of dynamic and flight physical behavior of new aircraft concepts, which is difficult with more theoretical methods. Furthermore, by using an SFD, the risks associated with full-scale testing in terms of cost and time are minimized. One of the new concepts developed for the SFD is Distributed electric propulsion (DEP). Here, the two jet engines are replaced by six electric propellers. As these can be used actively for control, this results in an over-actuated system. These propellers interact with the aerodynamics of the wing resulting in Propulsion airframe interaction (PAI) effects. Although using the PAI effects for control has the potential to improve capabilities, efficiency and robustness of the aircraft, research into controllers using these effects actively is limited.

This thesis, therefore, presents a new control method including control allocation for the DEP-SFD aircraft, based on the nonlinear Incremental nonlinear dynamic inversion (INDI) controller. INDI enables controlling the nonlinear dynamics of the DEP aircraft over the complete flight envelope with one controller. By feeding back real-time sensor measurements, robustness to modeling errors and external disturbances is increased. Using the Incremental nonlinear control allocation (INCA) method, the full control authority of the DEP can be used. This technique enables taking into account nonlinear allocation relations and control effector interactions, while solving the control allocation real-time, which is key in actively using the PAI effects for control. The INCA method is used for two performance improvements: tracking performance and propeller power efficiency. To compensate for actuator dynamics, this thesis implements an Model predictive control (MPC) controller which results in improved tracking and higher efficiency. The performance of the controller was analyzed in simulation, where a reference square signal input on the roll angle was applied, while minimizing the sideslip angle and maintaining a steady altitude and velocity. The INCA controller with MPC is compared to a conventional INDI controller, showing a significant decrease in rise time from 2.46 s to 0.703 s with minimal tracking error. Furthermore, the effective bandwidth of the system was increased from 0.186 Hz to 0.663 Hz and the power consumption reduced by 6.3%. Modeling uncertainties, external disturbances and a propeller fault were introduced to verify the robustness of the controller. Finally, the reference altitude and velocity were varied, demonstrating controller performance over a large part of the flight envelope.