TY - GEN
T1 - Adaptive Backstepping Control of Morphing Aircraft Based on RBF Neural Networks
AU - Li, Yiheng
AU - Liu, Dawei
AU - Zhou, Hang
AU - Guo, Tao
AU - Guo, Mutian
AU - Xia, Qunli
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - To enhance the disturbance rejection of morphing aircraft, an adaptive backstepping control method based on the RBF neural network is proposed. Firstly, the aerodynamic parameters and model parameters are modeled as functions of sweep angle, and the latter is expressed as pure function of time. Then, the RBF neural network adaptive algorithm is combined with the backstepping control to estimate the uncertain disturbance during the wing transition process, so as to compensate for the control law output by the backstepping control. Thereafter, the weight update law of the RBF adaptive algorithm is obtained by Lyapunov stability theory, and the stability of the control system is proved at the same time. Finally, comparative simulation experiments are used to show that the proposed control strategy is preferable, and the error between actual values and the estimated of the RBF adaptive algorithm can be controlled within 0.05%.
AB - To enhance the disturbance rejection of morphing aircraft, an adaptive backstepping control method based on the RBF neural network is proposed. Firstly, the aerodynamic parameters and model parameters are modeled as functions of sweep angle, and the latter is expressed as pure function of time. Then, the RBF neural network adaptive algorithm is combined with the backstepping control to estimate the uncertain disturbance during the wing transition process, so as to compensate for the control law output by the backstepping control. Thereafter, the weight update law of the RBF adaptive algorithm is obtained by Lyapunov stability theory, and the stability of the control system is proved at the same time. Finally, comparative simulation experiments are used to show that the proposed control strategy is preferable, and the error between actual values and the estimated of the RBF adaptive algorithm can be controlled within 0.05%.
KW - RBF neural network
KW - adaptive backstepping control
KW - command filter
KW - morphing aircraft
UR - http://www.scopus.com/inward/record.url?scp=85176007152&partnerID=8YFLogxK
U2 - 10.1109/ISAES58852.2023.10281270
DO - 10.1109/ISAES58852.2023.10281270
M3 - Conference contribution
AN - SCOPUS:85176007152
T3 - 2023 2nd International Symposium on Aerospace Engineering and Systems, ISAES 2023
SP - 195
EP - 201
BT - 2023 2nd International Symposium on Aerospace Engineering and Systems, ISAES 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Symposium on Aerospace Engineering and Systems, ISAES 2023
Y2 - 19 May 2023 through 21 May 2023
ER -