TY - GEN
T1 - Composite Learning Control for UAVs via Prescribed Performance
AU - Jiang, Tao
AU - Lin, Defu
AU - Chen, Hao
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Unmanned quadrotors have been widely used in numerous practical application scenes, and attracted a great interest in control community. Our work is to achieve finite-time trajectory tracking of quadrotors under perturbations. Finite-time control is achieved by prescribed performance technique. A new prescribed performance function, which owns finite-time convergence property, is defined. This scheme produces less-complex control design for finite-time control and possesses the advantages of prescribed performance control. Furthermore, the composite learning, which combines nonlinear disturbance observer and direct adaptive neural control, is applied to improve the approximation performance and enhance system robustness. In view of the cascade structure of quadrotor dynamics, command-filter-based backstepping framework is adopted, where the proposed control techniques are integrated into. Finally, several comparative simulations demonstrate the effectiveness and superiority of the proposed method.
AB - Unmanned quadrotors have been widely used in numerous practical application scenes, and attracted a great interest in control community. Our work is to achieve finite-time trajectory tracking of quadrotors under perturbations. Finite-time control is achieved by prescribed performance technique. A new prescribed performance function, which owns finite-time convergence property, is defined. This scheme produces less-complex control design for finite-time control and possesses the advantages of prescribed performance control. Furthermore, the composite learning, which combines nonlinear disturbance observer and direct adaptive neural control, is applied to improve the approximation performance and enhance system robustness. In view of the cascade structure of quadrotor dynamics, command-filter-based backstepping framework is adopted, where the proposed control techniques are integrated into. Finally, several comparative simulations demonstrate the effectiveness and superiority of the proposed method.
KW - Neural adaptive control
KW - composite learning
KW - finite-time prescribed performance
KW - quadrotor trajectory tracking
UR - http://www.scopus.com/inward/record.url?scp=85091902796&partnerID=8YFLogxK
U2 - 10.1109/ICSIDP47821.2019.9173151
DO - 10.1109/ICSIDP47821.2019.9173151
M3 - Conference contribution
AN - SCOPUS:85091902796
T3 - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
BT - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Y2 - 11 December 2019 through 13 December 2019
ER -