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dc.contributor.authorLakshmikanth, Geethalakshmi S.
dc.contributor.authorPadhi, Radhakant
dc.contributor.authorWatkins, John Michael
dc.contributor.authorSteck, James E.
dc.identifier.citationGeethalakshmi S. Lakshmikanth, Radhakant Padhi, John M. Watkins, and James E. Steck. "Adaptive Flight-Control Design Using Neural-Network-Aided Optimal Nonlinear Dynamic Inversion", Journal of Aerospace Information Systems, Vol. 11, No. 11 (2014), pp. 785-806. doi: 10.2514/1.I010165en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractA neural-network-aided nonlinear dynamic inversion-based hybrid technique of model reference adaptive control flight-control system design is presented in this paper. Here, the gains of the nonlinear dynamic inversion-based flight-control system are dynamically selected in such a manner that the resulting controller mimics a single network, adaptive control, optimal nonlinear controller for state regulation. Traditional model reference adaptive control methods use a linearized reference model, and the presented control design method employs a nonlinear reference model to compute the nonlinear dynamic inversion gains. This innovation of designing the gain elements after synthesizing the single network adaptive controller maintains the advantages that an optimal controller offers, yet it retains a simple closed-form control expression in state feedback form, which can easily be modified for tracking problems without demanding any a priori knowledge of the reference signals. The strength of the technique is demonstrated by considering the longitudinal motion of a nonlinear aircraft system. An extended single network adaptive control/nonlinear dynamic inversion adaptive control design architecture is also presented, which adapts online to three failure conditions, namely, a thrust failure, an elevator failure, and an inaccuracy in the estimation of C-M alpha. Simulation results demonstrate that the presented adaptive flight controller generates a near-optimal response when compared to a traditional nonlinear dynamic inversion controller.en_US
dc.description.sponsorshipNASA under award number NNXO9AP20A.en_US
dc.publisherAmerican Institute of Aeronautics and Astronauticsen_US
dc.relation.ispartofseriesJournal of Aerospace Information Systems;v.11:no.11
dc.titleAdaptive flight-control design using neural-network-aided optimal nonlinear dynamic inversionen_US
dc.rights.holder©2014 AIAA

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