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Paper Detail

Paper:	SPTM-P8.6
Session:	Adaptive Systems and Filtering II
Time:	Thursday, May 18, 14:00 - 16:00
Presentation:	Poster
Topic:	Signal Processing Theory and Methods: Adaptive Systems and Filtering
Title:	Convex-Optimization-Based Enforcement of Robust BIBO Stability on the AIC Scheme Using a Modified RLS Algorithm
Authors:	Nestor Perez Arancibia, University of California, Los Angeles, United States
Abstract:	This paper addresses the issues relating to the enforcement of robust BIBO stability when implementing the adaptive inverse control (AIC) scheme for noise cancelation. In this scheme, an adaptive FIR-form filter is added to a closed-loop system in order to reduce the output error caused by external disturbances. A Small-Gain-Theorem-based sufficient stability condition, which accounts for the feedback interaction between the time-varying adaptive filter and the unmodeled dynamics existing in the closed-loop plant, is derived. This condition leads to the formulation of a constrained convex optimization problem solvable recursively using a modified RLS algorithm that preserves the converge properties of the original RLS algorithm.