An asymptotic scaling analysis of LQ performance of an approximate adaptive control design

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Description/Abstract

We consider the adaptive tracking problem for a chain of integrators, where the uncertainty is static and functional. The uncertainty is specified by $L^2/L^\infty$ or weighted $L^2/L^\infty$ norm bounds. We analyse a standard Lyapunov-based adaptive design which utilises a function approximator to induce a parametric uncertainty, on which the adaptive design is completed. Performance is measured by a modified LQ cost functional, penalising both the tracking error and the control effort. With such a cost functional, it is shown that a standard control design has divergent performance when the resolution of a "mono-resolution" approximator is increased. The class of "mono-resolution" approximators includes models popular in applications. A general construction of a class of approximators and their associated controllers which have a uniformly bounded performance independent of the resolution of the approximator is given.