TY - JOUR
T1 - Adaptive Neural Prescribed-Time Control of Switched Nonlinear Systems with Mode-Dependent Average Dwell Time
AU - Zeng, Danping
AU - Liu, Zhi
AU - Chen, C. L.Philip
AU - Wang, Yaonan
AU - Zhang, Yun
AU - Wu, Zongze
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Most current adaptive neural control strategies for switched nonlinear systems, both finite-time and fixed-time, are limited by a conservatively estimated settling time. Besides, the convergence accuracy of these methods is only bounded but unknown and uncertain. This study proposes a neural adaptive prescribed-time control method to solve such a problem. Specifically, a critical design step is that the practical prescribed-time control problem is converted into a practical stabilization problem by developing a new singularity-avoidance error-dependent scalar transformation. Guided by this idea, an adaptive neural prescribed-time controller is constructed, ensuring prescribed transient behavior and all tracking errors to achieve preset accuracy within the prescribed time simultaneously. Furthermore, by utilizing extended multiple Lyapunov functions, a new mode-dependent average dwell time condition is derived to ensure that all signals in the controlled system remain bounded. Finally, simulations demonstrate the feasibility of the developed scheme.
AB - Most current adaptive neural control strategies for switched nonlinear systems, both finite-time and fixed-time, are limited by a conservatively estimated settling time. Besides, the convergence accuracy of these methods is only bounded but unknown and uncertain. This study proposes a neural adaptive prescribed-time control method to solve such a problem. Specifically, a critical design step is that the practical prescribed-time control problem is converted into a practical stabilization problem by developing a new singularity-avoidance error-dependent scalar transformation. Guided by this idea, an adaptive neural prescribed-time controller is constructed, ensuring prescribed transient behavior and all tracking errors to achieve preset accuracy within the prescribed time simultaneously. Furthermore, by utilizing extended multiple Lyapunov functions, a new mode-dependent average dwell time condition is derived to ensure that all signals in the controlled system remain bounded. Finally, simulations demonstrate the feasibility of the developed scheme.
KW - Adaptive neural control
KW - mode-dependent average dwell time (MDADT)
KW - predefined-time control
KW - prescribed-time control
KW - switched systems
UR - https://www.scopus.com/pages/publications/85167825167
U2 - 10.1109/TSMC.2023.3296442
DO - 10.1109/TSMC.2023.3296442
M3 - 文章
AN - SCOPUS:85167825167
SN - 2168-2216
VL - 53
SP - 7427
EP - 7440
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 12
ER -