Adaptive tracking control with quantized output observations and single unknown parameter

This paper studies the adaptive tracking control for systems with quantized output observations and one unknown parameter. A projection algorithm is proposed for parameter identification, based on which an adaptive control law is designed via the certainty equivalence principle. By use of the conditional expectation of the quantized observation with respect to the estimates, it is shown that the identification algorithm is both almost surely and mean square convergent, the closed-loop system is stable, and the adaptive tracking control is asymptotically optimal.