Linear deterministic adaptive control: fundamental limitations?

This paper is concerned with the achievable performance of adaptive control algorithms. We show that when the only uncertainty is in the form of fixed parameter errors, then there exists an adaptive feedback law whose performance can be made arbitrarily close to that achievable when the system is a priori known. The result is not intended as a practical strategy. Instead, we use it to make the, perhaps obvious, point that meaningful results on performance of adaptive control algorithms must account for non-ideal factors including, at a minimum, noise, parameter time variations and unstructured uncertainty.