The 7th IFAC Symposium on Intelligent Autonomous Vehicles 2010

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LMI-based design of a Neuro-Adaptive augmentation controller for an Unmanned Aerial Vehicle

Mario Luca Fravolini
University of Perugia

Simone Fiani
University of Perugia

Giampiero Campa
The Mathworks, El Segundo, CA, USA
United States

Abstract: This paper presents a practical approach for verifying worst-case tracking performance of neuro-adaptive systems in presence of bounded uncertainties. Boundeness of the tracking error vector within an a-priori specified compact domain is obtained by applying robust invariant set analysis to the uncertain linear plant where the uncertainty and NN reconstruction error are considered as norm bounded persistent uncertainties. In this framework it was possible to specify worst-case tracking error requirements via a set of LMIs and to systematically verify the specifications using a numerical LMI solver. The presented method was applied to the performance verification of an adaptive augmentation controller for the short term dynamics of an UAV model.


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