Supervised learning of perceptron and output feedback dynamic networks: a feedback analysis via the small gain theorem

1997 ◽  
Vol 8 (3) ◽  
pp. 612-622 ◽  
Author(s):  
M. Rupp ◽  
A.H. Sayed
Keyword(s):  
Supervised Learning ◽  
Output Feedback ◽  
Dynamic Networks ◽  
Feedback Analysis ◽  
Small Gain Theorem ◽  
Small Gain

scholarly journals Feedback Analysis of Radial Basis Functions Neural Networks via Small Gain Theorem

2008 ◽  
Vol 41 (2) ◽  
pp. 7463-7467
Author(s):  
Ali S. Saad Azhar ◽  
Muhammad Shafiq ◽  
Jamil M. Bakhashwain ◽  
Fouad M. AL-Sunni
Keyword(s):  
Neural Networks ◽  
Radial Basis Functions ◽  
Basis Functions ◽  
Feedback Analysis ◽  
Small Gain Theorem ◽  
Radial Basis ◽  
Small Gain

scholarly journals Robust control of a chemical reactor with uncertainties

2013 ◽  
Vol 6 (2) ◽  
pp. 194-201 ◽  
Author(s):  
Anna Vasičkaninová ◽  
Monika Bakošová
Keyword(s):  
Robust Control ◽  
Output Feedback ◽  
Chemical Reactor ◽  
Experimental Results ◽  
Nonlinear Processes ◽  
Control Techniques ◽  
Small Gain Theorem ◽  
Small Gain ◽  
Safe Control ◽  
Two Parameters

Abstract This work deals with the design and the application of a robust control to a chemical reactor. The reactor is exothermic one. There are two parameters with only approximately known values in the reactor. These parameters are the reaction enthalpies. Because of the presence of uncertainty, the robust output feedback is designed. Two robust controllers are designed, the first one is based on the small gain theorem and the second one uses the H2/H∞ control techniques. The presented experimental results confirm applicability of mentioned approaches to safe control of nonlinear processes.

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