Research has shown that neural networks can be used to improve upon approximate dynamic inversion controllers in the case of uncertain nonlinear systems. In one possible architecture, the neural network adaptively cancels linearization errors through online learning. Learning may be accomplished by a simple weight update rule derived from Lyapunov theory, thus assuring the stability of the closed-loop system. In the paper, the authors discuss the evolution of this methodology and its application in a bank-to-turn autopilot design for an agile antiair 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 online learning. Finally, the resulting control law is demonstrated in a nonlinear simulation and its performance is evaluated relative to a conventional gain-scheduled linear autopilot.
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