Paper Info
Title
Developing an IIR robust adaptive algorithm in the modified Filtered-x RLS form for active noise and vibration control systems
Abstract
In this paper, a robust adaptive algorithm for active noise and vibration control applications is proposed and the robust stability of the algorithm is analyzed using a combination of the small gain theorem and Popov's hyperstability theorem. The algorithm is developed based on the so-called Filtered-x RLS algorithm in the modified form. In design and analysis of the algorithm, it is assumed that the estimated model of the secondary path is associated with a set of uncertainties of additive structure; and sufficient conditions for stability of the algorithm are derived. In fact, by introducing a stabilizing filter, the aim is to design this filter in a way that the achieved sufficient conditions for robust stability are satisfied. The employed method is to transform the proposed control structure to an equivalent output error identification problem, and then formulate the governing adaptive algorithm in a way that is representable as a feedback control problem. In view of this approach, sufficient conditions for robust stability of the adaptive algorithm will be equivalent to find the conditions for the stability of the established feedback control system. The technique applied here to this end is established on the energy conservation relation that is valid for the general data models in adaptive filters.
Year
DOI
Venue
2011
10.1109/CDC.2011.6161126
Decision and Control and European Control Conference
Keywords
Field
DocType
feedback,robust control,vibration control,Filtered-x RLS,IIR robust adaptive algorithm,Popovs hyperstability theorem,active noise,additive structure,error identification problem,feedback control problem,robust stability,stabilizing filter,vibration control applications,vibration control systems
Mathematical optimization,Algorithm design,Computer science,Control theory,Adaptive filter,Adaptive control,Adaptive algorithm,Robust control,Hyperstability,Small-gain theorem,Recursive least squares filter
Conference
ISSN
ISBN
Citations 
0743-1546 E-ISBN : 978-1-61284-799-3
978-1-61284-799-3
1
PageRank 
References 
Authors
0.36
14
2
Name
Order
Citations
PageRank
A. Montazeri1306.67
Johann Reger24017.29