Robust output feedback control of nonlinear stochastic systems using neural networks

2003 ◽  
Vol 14 (1) ◽  
pp. 103-116 ◽  
Author(s):  
S. Battilotti ◽  
A. De Santis
Keyword(s):  
Neural Networks ◽  
Feedback Control ◽  
Output Feedback ◽  
Stochastic Systems ◽  
Output Feedback Control ◽  
Nonlinear Stochastic Systems

scholarly journals Prescribed Performance Fuzzy Adaptive Output-Feedback Control for Nonlinear Stochastic Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Lili Zhang ◽  
Shuai Sui ◽  
Shaocheng Tong
Keyword(s):  
Feedback Control ◽  
Output Feedback ◽  
Stochastic Systems ◽  
Output Feedback Control ◽  
Performance Bounds ◽  
Nonlinear Stochastic Systems ◽  
Prescribed Performance ◽  
Adaptive Output Feedback Control ◽  
Fuzzy State ◽  
Fuzzy Adaptive

A prescribed performance fuzzy adaptive output-feedback control approach is proposed for a class of single-input and single-output nonlinear stochastic systems with unmeasured states. Fuzzy logic systems are used to identify the unknown nonlinear system, and a fuzzy state observer is designed for estimating the unmeasured states. Based on the backstepping recursive design technique and the predefined performance technique, a new fuzzy adaptive output-feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin with the prescribed performance bounds. A simulation example is provided to show the effectiveness of the proposed approach.

Output Feedback Control of Fuzzy Stochastic Systems

2021 ◽  
pp. 43-58
Author(s):  
Xiaojie Su ◽  
Yao Wen ◽  
Yue Yang ◽  
Peng Shi
Keyword(s):  
Feedback Control ◽  
Output Feedback ◽  
Stochastic Systems ◽  
Output Feedback Control
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