TY - GEN
T1 - Adaptive neural dynamic surface control of morphing aircraft with input constraints
AU - Wu, Zhonghua
AU - Lu, Jingchao
AU - Shi, Jingping
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/12
Y1 - 2017/7/12
N2 - A robust adaptive neural dynamic surface control (DSC) approach is presented for the longitudinal dynamics of a morphing aircraft in the presence of unknown dynamics and input constraints. For the altitude subsystem, neural systems are utilized to approximate the unknown nonlinear functions with smooth robust compensations to counteract the lumped approximation errors. By combining dynamic surface control and minimal learning parameter techniques, a robust adaptive neural control scheme is proposed and a simple adaptive algorithm is constructed. Meanwhile, an auxiliary system is incorporated into the control scheme to overcome the problem of input saturation. The highlight is that the proposed neural controller not only owns less updated neural parameters, but also has the ability of handling input constraints. It is proved that all the signals in the closed-loop system are bounded. Simulation results demonstrate the effectiveness of the proposed control scheme.
AB - A robust adaptive neural dynamic surface control (DSC) approach is presented for the longitudinal dynamics of a morphing aircraft in the presence of unknown dynamics and input constraints. For the altitude subsystem, neural systems are utilized to approximate the unknown nonlinear functions with smooth robust compensations to counteract the lumped approximation errors. By combining dynamic surface control and minimal learning parameter techniques, a robust adaptive neural control scheme is proposed and a simple adaptive algorithm is constructed. Meanwhile, an auxiliary system is incorporated into the control scheme to overcome the problem of input saturation. The highlight is that the proposed neural controller not only owns less updated neural parameters, but also has the ability of handling input constraints. It is proved that all the signals in the closed-loop system are bounded. Simulation results demonstrate the effectiveness of the proposed control scheme.
KW - Auxiliary compensation system
KW - Dynamic surface control
KW - Minimal parameter learning
KW - Neural network
UR - https://www.scopus.com/pages/publications/85028081361
U2 - 10.1109/CCDC.2017.7978057
DO - 10.1109/CCDC.2017.7978057
M3 - 会议稿件
AN - SCOPUS:85028081361
T3 - Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017
SP - 6
EP - 12
BT - Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 29th Chinese Control and Decision Conference, CCDC 2017
Y2 - 28 May 2017 through 30 May 2017
ER -