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- W125981401 abstract "Abstract : The prevalent method of synthesizing nonlinear missile autopilots is by gain-scheduling linear designs. Although this approach has proven successful in numerous applications, the desire to continually improve performance without incurring additional cost suggests the need for a new design paradigm. An opportunity to address this need has been identified from previous research which employed neural network technology to augment approximate dynamic inversion controllers. In the one architecture a neural network adaptively cancels linearization errors through on-line learning, which may be accomplished by a weight update rule derived from Lyapunov theory. This effectively guarantees stability of the closed-loop system. This paper concerns a similar implementation in which neural networks function instead to improve command tracking of gain-scheduled control laws. This theoretical development is then specialized to the problem of synthesizing a bank-to-turn autopilot for an agile anti-air missile. Finally, the resulting hybrid control law is demonstrated in a nonlinear simulation and its performance is evaluated relative to that of the unaugmented gain-scheduled autopilot." @default.
- W125981401 created "2016-06-24" @default.
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- W125981401 date "1998-01-01" @default.
- W125981401 modified "2023-09-27" @default.
- W125981401 title "Adaptive Nonlinear Autopilot for Anti-Air Missiles." @default.
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