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• W1505032840 abstract "In this paper, we propose to use different optimization objectives to train a neural network to approximate the nonlinear inverse dynamics of a system. We implement and test this approach using radial basis function (RBF) networks to approximate the nonlinear inverse dynamics of a simulated high performance aircraft. The synthesised inverse dynamics controller performs well in tracking simulated trajectories. The use of optimization objectives allows us to deal with the issues of non-invertibility and stability of the inverse dynamics and to synthesise a nonlinear controller which has the desired performance characteristics." @default.
• W1505032840 created "2016-06-24" @default.
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• W1505032840 date "2005-08-24" @default.
• W1505032840 modified "2023-09-27" @default.
• W1505032840 title "Learning the nonlinear inverse flight dynamics using radial basis functions" @default.
• W1505032840 cites W2007700211 @default.
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• W1505032840 doi "https://doi.org/10.1109/acc.1995.533789" @default.
• W1505032840 hasPublicationYear "2005" @default.
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