Adaptive High-Gain Observers via Discete time Identifier

Ingallina, Alessandro (2019) Adaptive High-Gain Observers via Discete time Identifier. [Laurea magistrale], Università di Bologna, Corso di Studio in Automation engineering / ingegneria dell’automazione [LM-DM270], Documento full-text non disponibile
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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.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Ingallina, Alessandro
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
adaptive high gain observer,discrete time identifiers,adaptation problem,artificial neural networks
Data di discussione della Tesi
15 Marzo 2019

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