.png){kind=icon}
.png){kind=icon}
Return
Prescribed-Time Composite Learning Control for Uncertain Strict-Feedback Nonlinear Systems
.png){kind=icon}
PRE
AI
Z
Y
X
DOI：10.1002/acs.70117.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article proposes a prescribed-time (PT) neural network composite learning tracking control scheme for a class of strict-feedback nonlinear systems with functional uncertainties. Specifically, a PT composite learning framework is constructed to improve the estimation performance of the unknown nonlinear functions under the weaker interval excitation (IE) condition. To guarantee PT tracking performance, a PT controller with time-varying bounded gains is designed to ensure that the tracking error converges to an arbitrarily small neighborhood of zero within the prescribed time, independent of initial conditions. Furthermore, a PT dynamic surface filter is developed to overcome the explosion of complexity inherent in traditional backstepping designs. Finally, numerical simulations and real-time experiments on a permanent magnet synchronous motor (PMSM) platform are conducted to validate the effectiveness and practical applicability of the proposed control scheme.
Keywords:
composite learning
functional uncertainties
neural network
prescribed-time control
strict-feedback systems
Journal
IF:
3.8
Papers:
2.5K
Citations:
3.6K
Organization
Cited Papers