Paper Info
Title
Adaptive Output-Feedback Control For A Class Of Stochastic Nonlinear Systems With Unknown Control Directions And Hysteresis Input
Abstract
This paper is concerned with an adaptive neural output-feedback control for a class of stochastic nonlinear systems with unknown control directions and hysteresis input. An output-feedback controller is developed for stochastic nonlinear via using radial basis function neural networks (RBFNNs) and adaptive backstepping method. A state observer is designed to estimate the unmeasurable system state signals. Nussbaum gain technique is employed to deal with the unknown control directions. Simultaneously, the backlash-like hysteresis input control in this paper is considered. An adaptive controller is designed to ensure that the output tracking error converges on a small region of the origin. Finally, the control scheme ensures that all signals in the closed-loop systems are semi-global uniformly ultimately bounded. Results of simulation cases are presented to prove the effectivity of the theoretical analysis.
Year
DOI
Venue
2021
10.1080/00207721.2020.1837287
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Keywords
DocType
Volume
Output-feedback control, unknown control directions, stochastic disturbances, hysteresis input, RBFNNs
Journal
52
Issue
ISSN
Citations 
3
0020-7721
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Fei Shen1319.29
Xinjun Wang2104.49
Xinghui Yin301.69