Increasing predictability and performance in UAS flight contingencies using AIDL and MPC
Conference
Gavilan, F, Vazquez, R, Lobato, A et al. (2018). Increasing predictability and performance in UAS flight contingencies using AIDL and MPC
. 10.2514/6.2018-1586
Gavilan, F, Vazquez, R, Lobato, A et al. (2018). Increasing predictability and performance in UAS flight contingencies using AIDL and MPC
. 10.2514/6.2018-1586
A rich and now mature formal language for expressing aircraft intent called AIDL has been demonstrated to enable much greater degrees of predictability which can be vital when relying upon a calculation of aircraft trajectories for aircraft coordination in air traffic management and, in particular, contingency management scenarios. The predictability of an aircraft intent formulated in AIDL can be further ensured and guaranteed through the use of an accompanying model predictive flight control architecture. Model Predictive Control (MPC) is challenging to implement for flight control because it requires the online solution of an associated nonlinear optimization problem. However, recent advances in MPC techniques also permit their implementation now in low-cost flight hardware. To satisfy computational requirements, a novel flight control architecture that separates the flight control problem in two different controllers (top level and low level, according to the natural time scales of the system) is presented within this paper along with simulation performance and hardware-in-the-loop test results. It is argued that this combination and architecture can prove to be a valuable solution for outstanding problems in UAS contingency management, a requirement for UAS insertion into civil airspace.
