Stabilisation of SDEs and applications to synchronisation of stochastic neural network driven by G-Brownian motion with state-feedback control

2018 ◽  
Vol 50 (2) ◽  
pp. 273-282 ◽  
Author(s):  
Yong Ren ◽  
Wensheng Yin ◽  
Dongjin Zhu
Keyword(s):  
Neural Network ◽  
Brownian Motion ◽  
Feedback Control ◽  
State Feedback ◽  
State Feedback Control ◽  
Stochastic Neural Network

Neural Network-based State Feedback Control of Parallel UPS System

2021 ◽  
Author(s):  
Hui Fang ◽  
Te Zhu ◽  
Long Ran ◽  
He Peng ◽  
Hongting Hua ◽  
...  
Keyword(s):  
Neural Network ◽  
Feedback Control ◽  
State Feedback ◽  
State Feedback Control

NN approach to state-feedback control of high-order non-linear time-delay systems with time-varying control coefficient

2016 ◽  
Vol 40 (1) ◽  
pp. 163-170 ◽  
Author(s):  
Min Huifang ◽  
Duan Na
Keyword(s):  
Neural Network ◽  
Feedback Control ◽  
State Feedback ◽  
Linear Time ◽  
High Order ◽  
State Feedback Control ◽  
Delay Systems ◽  
Time Varying ◽  
Control Coefficient ◽  
Non Linear

This paper considers the adaptive state-feedback control problem for a class of high-order non-linear systems with unknown control coefficient and time delays. By applying the neural network approximation method and the Nussbaum function approach, the restrictions on non-linear functions and the conditions on the time-varying control coefficient are largely relaxed. In addition, an adaptive neural network state-feedback controller with only one adaptive parameter is successfully constructed by introducing proper Lyapunov–Krasovskii functionals and using the backstepping technique. The proposed scheme guarantees the closed-loop system to be semi-globally uniformly ultimately bounded. Finally, a simulation example demonstrates the effectiveness of the controller.

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