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Performance-oriented adaptive neural augmentation of an existing flight control system

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3 Author(s)
Fravolini, M.L. ; Dept. of Electron. & Inf. Eng., Univ. of Perugia, Perugia, Italy ; Campa, G. ; Napolitano, M.R.

Control systems for safety-critical applications, including the ones relying on adaptive elements, have to be certified against strict performance and safety requirements. This study presents a practical approach for the design of a neuro-adaptive element with the specific purpose of safely recovering the performance of a reference model in presence of bounded uncertainties. The boundedness of the tracking error vector within an a-priori specified compact domain is enforced by applying robust invariant set analysis to the uncertain linear plant where the adaptive neural contribution is considered as an amplitude-bounded persistent disturbance. In this framework, tracking error requirements are specified via a set of LMI constraints and maximal allowed amplitudes for the adaptive control are computed using a numerical LMI solver. A specific neural network on-line learning and output confinement algorithm is also proposed to keep the adaptive control within selected amplitudes; as a result, the overall closed loop system has a guaranteed worst-case transient response. The proposed approach has been successfully applied to the design of a multi input multi output (MIMO) augmentation adaptive element that improves the performance of a pre-existing tracking controller for a research aircraft model.

Published in:

Control Theory & Applications, IET  (Volume:5 ,  Issue: 16 )

Date of Publication:

November 3 2011

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