TY - GEN
T1 - Prescribed Performance Event-Triggered Trajectory Tracking Control for Stratospheric Airship
AU - Sun, Peihao
AU - Zhu, Ming
AU - Zhang, Yifei
AU - Chen, Tian
AU - Zheng, Zewei
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This study presents a novel trajectory tracking controller for a stratospheric airship, focusing on event-triggered control and prescribed performance. The proposed controller, based on the framework of prescribed performance backstepping method, combines a second-order derivative filter and a radial basis function neural network. The controller design incorporates an event-triggered strategy to achieve prescribed performance objectives. The derivative filter addresses the computational complexity associated with the virtual control law, while the radial basis function neural network estimates unknown terms. Through Lyapunov analysis, the stability and non-Zeno behavior of the system are established. Simulation results confirm the effectiveness of the designed controller in achieving the desired trajectory tracking objectives.
AB - This study presents a novel trajectory tracking controller for a stratospheric airship, focusing on event-triggered control and prescribed performance. The proposed controller, based on the framework of prescribed performance backstepping method, combines a second-order derivative filter and a radial basis function neural network. The controller design incorporates an event-triggered strategy to achieve prescribed performance objectives. The derivative filter addresses the computational complexity associated with the virtual control law, while the radial basis function neural network estimates unknown terms. Through Lyapunov analysis, the stability and non-Zeno behavior of the system are established. Simulation results confirm the effectiveness of the designed controller in achieving the desired trajectory tracking objectives.
KW - event-trigger control
KW - neural networks
KW - prescribed performance
KW - stratospheric airship
KW - trajectory tracking
UR - https://www.scopus.com/pages/publications/85180126575
U2 - 10.1109/ICUS58632.2023.10318510
DO - 10.1109/ICUS58632.2023.10318510
M3 - 会议稿件
AN - SCOPUS:85180126575
T3 - Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023
SP - 363
EP - 369
BT - Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023
A2 - Song, Rong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Unmanned Systems, ICUS 2023
Y2 - 13 October 2023 through 15 October 2023
ER -