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2013 | OriginalPaper | Buchkapitel

29. Effect of Choice of Basis Functions in Neural Network for Capturing Unknown Function for Dynamic Inversion Control

verfasst von : Gandham Ramesh, P. N. Dwivedi, P. Naveen Kumar, R. Padhi

Erschienen in: Recent Advancements in System Modelling Applications

Verlag: Springer India

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Abstract

The basic requirement for an autopilot is fast response and minimum steady state error for better guidance performance. The highly nonlinear nature of the missile dynamics due to the severe kinematic and inertial coupling of the missile airframe as well as the aerodynamics has been a challenge for an autopilot that is required to have satisfactory performance for all flight conditions in probable engagements. Dynamic inversion is very popular nonlinear controller for this kind of scenario. But the drawback of this controller is that it is sensitive to parameter perturbation. To overcome this problem, neural network has been used to capture the parameter uncertainty on line. The choice of basis function plays the major role in capturing the unknown dynamics. Here in this paper, many basis function has been studied for approximation of unknown dynamics. Cosine basis function has yield the best response compared to any other basis function for capturing the unknown dynamics. Neural network with Cosine basis function has improved the autopilot performance as well as robustness compared to Dynamic inversion without Neural network.

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Metadaten
Titel
Effect of Choice of Basis Functions in Neural Network for Capturing Unknown Function for Dynamic Inversion Control
verfasst von
Gandham Ramesh
P. N. Dwivedi
P. Naveen Kumar
R. Padhi
Copyright-Jahr
2013
Verlag
Springer India
DOI
https://doi.org/10.1007/978-81-322-1035-1_29