A New Recursive Identification Algorithm for Singularity Free Adaptive Control

Reportedly, guaranteeing the controllability of the estimated system is a crucial problem in adaptive control. In this paper, we introduce a least squares-based identification algorithm for stochastic SISO systems, which secures the uniform controllability of the estimated system and presents closed-loop identification properties similar to those of the least squares algorithm. The proposed algorithm is recursive and, therefore, easily implementable. Its use, however, is confined to cases in which the parameter uncertainly is highly structured. Keywords: uniform controllability, adaptive control, least squares identification, stochastic systems, recursive identification algorithms 1 Introduction It is well known ([6, 5, 13, 14, 4, 2, 10, 17, 15, 1, 18]) that the possible occurrence of polezero cancellations in the estimated model hampers the development of adaptive control algorithms for possibly nonminimum-phase systems. As a matter of fact, many wellestablished stability and perfor..