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Embedding norm-bounded Model Predictive Control allocation strategy for the High Altitude Performance Demonstrator (HAPD) Aircraft | IEEE Conference Publication | IEEE Xplore

Embedding norm-bounded Model Predictive Control allocation strategy for the High Altitude Performance Demonstrator (HAPD) Aircraft


Abstract:

In this paper the implementation of a robust Model Predictive Control (MPC) algorithm for constrained uncertain discrete-time linear systems subject to norm-bounded model...Show More

Abstract:

In this paper the implementation of a robust Model Predictive Control (MPC) algorithm for constrained uncertain discrete-time linear systems subject to norm-bounded model uncertainties is used to deal with the control allocation problem of an High Altitude Performance Demonstrator (HAPD) unmanned aircraft with redundancy control surfaces. Specifically, the HAPD nonlinear model is described by means of LFR differential inclusions achieved via embedding arguments while the surface deflection amplitude limitations are formulated in terms of convex constraints. As a consequence, the overall control problem can be recast in terms of Linear Matrix Inequalities (LMIs) that are affordable from a computational point of view.
Date of Conference: 10-13 December 2013
Date Added to IEEE Xplore: 10 March 2014
ISBN Information:
Print ISSN: 0191-2216
Conference Location: Firenze, Italy

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