Adaptive High-Gain Observers via Discete time Identifier

The system's state observation is one of the most important problem in control theory, and it becomes extremely challenging when the system model is not entirely known. For linear systems the problem is solved by using Luenberger observer in a deterministic framework and by Kalman filter in a stochastic framework, while for nonlinear systems, the observation problem is still a research topic. The aim of this thesis is to give a framework, in which the adaptation problem, relative to the model unknowns, can be performed by system identification techniques. In particular, in this thesis we develop and implement adaptive observers design, that uses "universal approximator" to perform the adaptation problem. Moreover, we present simulations on the performance of the proposed observer.