Adaptive feedback control of ultrafast semiconductor nonlinearities

2000 ◽  
Vol 77 (7) ◽  
pp. 924 ◽  
Author(s):  
J. Kunde ◽  
B. Baumann ◽  
S. Arlt ◽  
F. Morier-Genoud ◽  
U. Siegner ◽  
...  
Keyword(s):  
Feedback Control ◽  
Adaptive Feedback ◽  
Adaptive Feedback Control

Open-loop adaptive feedback control of deposited zinc in hot-dip galvanizing

1993 ◽  
Vol 1 (5) ◽  
pp. 779-790 ◽  
Author(s):  
C. Fenot ◽  
F. Rolland ◽  
G. Vigneron ◽  
I.D. Landau
Keyword(s):  
Feedback Control ◽  
Open Loop ◽  
Adaptive Feedback ◽  
Adaptive Feedback Control

Adaptive Feedback Control of Nonholonomic Systems with Uncertainties

2014 ◽  
Vol 971-973 ◽  
pp. 337-340
Author(s):  
Gui Ling Ju ◽  
Wei Hai Sun ◽  
Jian Du ◽  
Yun Chang Hang
Keyword(s):  
Feedback Control ◽  
Nonholonomic Systems ◽  
The State ◽  
Output Measurement ◽  
Adaptive Feedback ◽  
Adaptive Feedback Control ◽  
Strongly Nonlinear ◽  
Output Feedback Controller ◽  
Nonlinear Uncertainties ◽  
Switching Control Strategy

This paper deals with the adaptive feedback control for the nonholonomic systems with strongly nonlinear uncertainties. The state-input scaling technique and back-stepping approach are used to design the output feedback controller. In order to make the state scaling effective, a new switching control strategy based on the output measurement of the first subsystem is employed.

scholarly journals Adaptive Feedback Control for Synchronization of Chaotic Neural Systems with Parameter Mismatches

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Qian Ye ◽  
Zhengxian Jiang ◽  
Tiane Chen
Keyword(s):  
Feedback Control ◽  
Invariance Principle ◽  
Neural Systems ◽  
Adaptive Feedback ◽  
Adaptive Feedback Control ◽  
Synchronization Problem ◽  
Feedback Method

This work pertains to the study of the synchronization problem of a class of coupled chaotic neural systems with parameter mismatches. By means of an invariance principle, a rigorous adaptive feedback method is explored for synchronization of a class of coupled chaotic delayed neural systems in the presence of parameter mismatches. Finally, the performance is illustrated with simulations in a two-order neural systems.

scholarly journals Adaptive stochastic synchronization of delayed reaction–diffusion neural networks

2020 ◽  
Vol 53 (3-4) ◽  
pp. 378-389 ◽  
Author(s):  
Weiyuan Zhang ◽  
Junmin Li ◽  
Jinghan Sun ◽  
Minglai Chen
Keyword(s):  
Neural Networks ◽  
Feedback Control ◽  
Sufficient Conditions ◽  
Reaction Diffusion ◽  
Linear Matrix ◽  
Output Coupling ◽  
Delayed Reaction ◽  
Adaptive Feedback ◽  
Adaptive Feedback Control ◽  
Stochastic Synchronization

In this paper, we deal with the adaptive stochastic synchronization for a class of delayed reaction–diffusion neural networks. By combing Lyapunov–Krasovskii functional, drive-response concept, the adaptive feedback control scheme, and linear matrix inequality method, we derive some sufficient conditions in terms of linear matrix inequalities ensuring the stochastic synchronization of the addressed neural networks. The output coupling with delay feedback and the update laws of parameters for adaptive feedback control are proposed, which will be of significance in the real application. The novel Lyapunov–Krasovskii functional to be constructed is more general. The derived results depend on the measure of the space, diffusion effects, and the upper bound of derivative of time-delay. Finally, an illustrated example is presented to show the effectiveness and feasibility of the proposed scheme.

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