TY - GEN
T1 - Robust Prescribed Performance Control and Adaptive Learning for the Longitudinal Dynamics of Fixed-Wing UAVs
AU - Tzeranis, S.
AU - Trakas, P. S.
AU - Papageorgiou, X.
AU - Kyriakopoulos, K. J.
AU - Bechlioulis, C. P.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The objective of this work is to simultaneously control and identify the nonlinear longitudinal dynamics of small-scale fixed-wing Unmanned Aerial Vehicles (UAVs). The main difficulty in this endeavor lies in the satisfaction of the Persistence of Excitation (PE) condition, which eventually ensures accurate learning. Towards this direction, our key components comprise Radial Basis Function-Neural Networks (RBF-NNs), which are suitable mathematical models for universal function approximation, alongside with: i) the recently developed Dynamic Regression Extension and Mixing (DREM) technique; a new procedure for designing parameter estimators with enhanced performance, as well as ii) a novel control design for the longitudinal UAV dynamics utilizing the Prescribed Performance Control (PPC) methodology, which enables robust trajectory tracking with predetermined transient and steady state quality, even in the presence of model uncertainties.
AB - The objective of this work is to simultaneously control and identify the nonlinear longitudinal dynamics of small-scale fixed-wing Unmanned Aerial Vehicles (UAVs). The main difficulty in this endeavor lies in the satisfaction of the Persistence of Excitation (PE) condition, which eventually ensures accurate learning. Towards this direction, our key components comprise Radial Basis Function-Neural Networks (RBF-NNs), which are suitable mathematical models for universal function approximation, alongside with: i) the recently developed Dynamic Regression Extension and Mixing (DREM) technique; a new procedure for designing parameter estimators with enhanced performance, as well as ii) a novel control design for the longitudinal UAV dynamics utilizing the Prescribed Performance Control (PPC) methodology, which enables robust trajectory tracking with predetermined transient and steady state quality, even in the presence of model uncertainties.
KW - Dynamic Regression
KW - Extension and Mixing
KW - Prescribed Performance Control
KW - System Identification
UR - http://www.scopus.com/inward/record.url?scp=85146491876&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146491876&partnerID=8YFLogxK
U2 - 10.1109/ICSC57768.2022.9993866
DO - 10.1109/ICSC57768.2022.9993866
M3 - Conference contribution
AN - SCOPUS:85146491876
T3 - 2022 10th International Conference on Systems and Control, ICSC 2022
SP - 391
EP - 396
BT - 2022 10th International Conference on Systems and Control, ICSC 2022
A2 - Mehdi, Driss
A2 - Outbib, Rachid
A2 - El-Hajjaji, Ahmed
A2 - Busvelle, Eric
A2 - Noura, Hassan
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Systems and Control, ICSC 2022
Y2 - 23 November 2022 through 25 November 2022
ER -