scholarly journals Higher Order Neural Units for Efficient Adaptive Control of Weakly Nonlinear Systems

Author(s):  
Ivo Bukovsky ◽  
Jan Voracek ◽  
Kei Ichiji ◽  
Homma Noriyasu
Author(s):  
Frank L. Lewis ◽  
Hongwei Zhang ◽  
Kristian Hengster-Movric ◽  
Abhijit Das

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
Shangtai Jin ◽  
Zhongsheng Hou ◽  
Ronghu Chi

In this work, a novel higher-order model-free adaptive control scheme is presented based on a dynamic linearization approach for a class of discrete-time single input and single output (SISO) nonlinear systems. The control scheme consists of an adaptive control law, a parameter estimation law, and a reset mechanism. The design and analysis of the proposed control approach depends merely on the measured input and output data of the controlled plant. The control performance is improved by using more information of control input and output error measured from previous sampling time instants. Rigorous mathematical analysis is developed to show the bounded input and bounded output (BIBO) stability of the closed-loop system. Two simulation comparisons show the effectiveness of the proposed control scheme.


1999 ◽  
Vol 09 (03) ◽  
pp. 519-531 ◽  
Author(s):  
K. YAGASAKI ◽  
T. ICHIKAWA

We consider periodically forced, weakly nonlinear systems and perform higher-order averaging analyses. Especially, we describe an algorithm for computing the higher-order averaging terms by the Lie transforms. The necessary computations can be implemented on a developed package of the computer algebra system, Mathematica. We also give three examples for two Duffing-type oscillators with the primary or ultra-subharmonic resonance and a two-degree-of-freedom system with internal and external resonances, to demonstrate our results.


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