Finite-model adaptive control using an LS-like algorithm

Adaptive control problem of a class of discrete-time nonlinear uncertain systems, of which the internal uncertainty can be characterized by a finite set of functions, is formulated and studied by using an least squares (LS)-like algorithm to design the feedback control law. For the finite-model adaptive control problem, this algorithm is proposed as an extension of counterpart of traditional LS algorithm. Stability in sense of pth mean for the closed-loop system is proved under a so-called linear growth assumption, which is shown to be necessary in general by a counter-example constructed in this paper. The main results have been also applied to parametric cases, which demonstrate how to bridge the non-parametric case and parametric case.