Sampled-data stabilization for a class of stochastic nonlinear systems based on the approximate discrete-time models

2016 ◽  
Author(s):  
Xinxin Fu ◽  
Yu Kang ◽  
Pengfei Li
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Stochastic Nonlinear Systems ◽  
Sampled Data ◽  
Discrete Time Models

scholarly journals Semiglobal exponential input-to-state stability of sampled-data systems based on approximate discrete-time models

Automatica ◽  
2021 ◽  
Vol 131 ◽  
pp. 109742
Author(s):  
Alexis J. Vallarella ◽  
Paula Cardone ◽  
Hernan Haimovich
Keyword(s):  
Discrete Time ◽  
Data Systems ◽  
Sampled Data ◽  
Discrete Time Models

Global sampled-data output feedback stabilization for a class of switched stochastic nonlinear systems with quantized input and unknown output gain

2019 ◽  
Vol 41 (16) ◽  
pp. 4511-4520
Author(s):  
Yan Jiang ◽  
Junyong Zhai
Keyword(s):  
Nonlinear Systems ◽  
Output Feedback ◽  
Output Feedback Control ◽  
Sampling Period ◽  
Feedback Stabilization ◽  
Design Parameters ◽  
Stochastic Nonlinear Systems ◽  
Control Input ◽  
Sampled Data ◽  
Data Output

This paper aims at addressing the sampled-data output feedback control problem for a class of uncertain switched stochastic nonlinear systems, whose control input is quantized by a logarithmic quantizer and the output gain cannot be precisely known. We design a compensator with the quantized information. With the help of the feedback domination approach and the backstepping design method, a sampled-data output feedback controller is constructed with appropriate design parameters and a maximum sampling period to guarantee the global exponential stability in mean square of the closed-loop system under arbitrary switching. Finally, a numerical example is given to illustrate the effectiveness of the proposed scheme.

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