TY - JOUR
T1 - Addressing Unmodeled Path-Following Dynamics via Adaptive Vector Field
T2 - A UAV Test Case
AU - Fari, Stefano
AU - Wang, Ximan
AU - Roy, Spandan
AU - Baldi, Simone
N1 - Accepted Author Manuscript
PY - 2020
Y1 - 2020
N2 - The actual performance of model-based path-following methods for unmanned aerial vehicles (UAVs) shows considerable dependence on the wind knowledge and on the fidelity of the dynamic model used for design. This study analyzes and demonstrates the performance of an adaptive vector field (VF) control law which can compensate for the lack of knowledge of the wind vector and for the presence of unmodeled course angle dynamics. Extensive simulation experiments, calibrated on a commercial fixed-wing UAV and proven to be realistic, show that the new VF method can better cope with uncertainties than its standard version. In fact, while the standard VF approach works perfectly for ideal first-order course angle dynamics (and perfect knowledge of the wind vector), its performance degrades in the presence of unknown wind or unmodeled course angle dynamics. On the other hand, the estimation mechanism of the proposed adaptive VF effectively compensates for wind uncertainty and unmodeled dynamics, sensibly reducing the path-following error as compared to the standard VF.
AB - The actual performance of model-based path-following methods for unmanned aerial vehicles (UAVs) shows considerable dependence on the wind knowledge and on the fidelity of the dynamic model used for design. This study analyzes and demonstrates the performance of an adaptive vector field (VF) control law which can compensate for the lack of knowledge of the wind vector and for the presence of unmodeled course angle dynamics. Extensive simulation experiments, calibrated on a commercial fixed-wing UAV and proven to be realistic, show that the new VF method can better cope with uncertainties than its standard version. In fact, while the standard VF approach works perfectly for ideal first-order course angle dynamics (and perfect knowledge of the wind vector), its performance degrades in the presence of unknown wind or unmodeled course angle dynamics. On the other hand, the estimation mechanism of the proposed adaptive VF effectively compensates for wind uncertainty and unmodeled dynamics, sensibly reducing the path-following error as compared to the standard VF.
KW - Adaptive vector field
KW - fixed-wing unmanned aerial vehicles (UAV)
KW - path-following
KW - unmodeled course angle dynamics
UR - http://www.scopus.com/inward/record.url?scp=85083439148&partnerID=8YFLogxK
U2 - 10.1109/TAES.2019.2925487
DO - 10.1109/TAES.2019.2925487
M3 - Article
AN - SCOPUS:85083439148
VL - 56
SP - 1613
EP - 1622
JO - IEEE Transactions on Aerospace and Electronic Systems
JF - IEEE Transactions on Aerospace and Electronic Systems
SN - 0018-9251
IS - 2
ER -