Paper Info
Title
Adaptive Neural Control for Switched Nonlinear Systems with Unmodeled Dynamics and Unknown Output Hysteresis
Abstract
This paper aims at addressing the adaptive neural control problem for switched nonlinear systems with output hysteresis and unmodeled dynamics. The switching law in this study is arbitrary. In our model, the unmodeled dynamics are assumed to be of input-to-state practical stability (ISpS). With the help of this assumption, a dynamic normalizing signal is constructed to dominate the unmodeled dynamics. And then, a direct adaptive neural state-feedback control scheme is developed with the help of approximation-based backstepping. The stability analysis shows that the system output is convergent to an adjustable small region of zero asymptotically, and furthermore, all the closed-loop signals are bounded. Finally, we further present two simulation examples to verify the effectiveness of our control scheme.
Year
DOI
Venue
2019
10.1016/j.neucom.2019.02.057
Neurocomputing
Keywords
Field
DocType
Switched nonlinear system,Arbitrary switching,Adaptive neural control,Unmodeled dynamics,Output hysteresis
Neural control,Backstepping,Nonlinear system,Pattern recognition,Control theory,Hysteresis,Artificial intelligence,Mathematics,Bounded function
Journal
Volume
ISSN
Citations 
341
0925-2312
3
PageRank 
References 
Authors
0.37
31
5
Name
Order
Citations
PageRank
Ziliang Lyu1251.94
Zhi Liu2107853.09
Yun Zhang357630.23
C. L. Philip Chen44022244.76
C. L. Philip Chen54022244.76