Neural-adaptive nonlinear autopilot design for an agile anti-air missile

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Previous research has shown that neural networks can be used to improve upon approximate dynamic inversion controllers in the case of uncertain nonlinear systems. In the one possible architecture, the neural network adaptively cancels linearization errors through on-line learning. Learning may be accomplished by a simple weight update rule derived from Liapunov theory, thus assuring the stability of the closed-loop system. In this paper, the authors apply this methodology to design a bank-to-turn autopilot for an agile anti-air missile. First, a control scheme based on approximate inversion of the vehicle dynamics is presented. This nonlinear control system is then augmented by the addition of a feedforward neural network with on-line learning. Finally, the resulting control law is demonstrated in a nonlinear simulation, and its performance is evaluated relative to a more traditional gain-scheduled linear autopilot. (Author)