Adaptive Pole Placement by Means of a Simple, Singularity Free, Identiication Algorithm

Reportedly, guaranteeing the controllability of the estimated system is a crucial problem in adaptive control. In this paper, we introduce a recursive least squares-based identiication algorithm for stochastic SISO systems , which secures the uniform controllability of the estimated system and presents closed-loop identiica-tion properties similar to those of the least squares algorithm. The proposed algorithm is recursive and, therefore, easily implementable. Its use, however, is connned to cases in which the parameter uncertainly is highly structured. This new identiication algorithm can be safely used in adaptive control applications. As a matter of fact, we introduce a pole placement adaptive control scheme equipped with such an algorithm and prove a pathwise stability result for the so-obtained closed-loop system.