An adaptive H∞ controller design for bank-to-turn missiles using ridge Gaussian neural networks
Resource
IEEE Transactions on Neural Networks 15 (6): 1507-1516
Journal
IEEE Transactions on Neural Networks
Journal Volume
15
Journal Issue
6
Pages
1507-1516
Date Issued
2004
Date
2004
Author(s)
Lin, Chuan-Kai
Abstract
A new autopilot design for bank-to-turn (BTT) missiles is presented. In the design of autopilot, a ridge Gaussian neural network with local learning capability and fewer tuning parameters than Gaussian neural networks is proposed to model the controlled nonlinear systems. We prove that the proposed ridge Gaussian neural network, which can be a universal approximator, equals the expansions of rotated and scaled Gaussian functions. Although ridge Gaussian neural networks can approximate the nonlinear and complex systems accurately, the small approximation errors may affect the tracking performance significantly. Therefore, by employing the Hinfinity control theory, it is easy to attenuate the effects of the approximation errors of the ridge Gaussian neural networks to a prescribed level. Computer simulation results confirm the effectiveness of the proposed ridge Gaussian neural networks-based autopilot with Hinfinity stabilization.
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Type
journal article
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