To provide high-level focus to distributed space system flight dynamics and control research, several Ijenchmark problems are suggested. These problems are not specific to any current or proposed mission, but instead are intended to capture high-level features that would be generic to many similar missions.

This paper treats the design of online adaptive neural networks for use in a nonlinear helicopter ight control architecture. Emphasis is given to network architecture and the e ect that varying the adaptation gain has on performance. Conclusions are based on a nonlinear evaluation model of an attack helicopter, and a metric that measures the networks ability t o cancel the e ect of modeling errors for a complicated maneuver. The network is shown to provide nearly perfect tracking in the face of signi cant modeling errors and additionally to cancel the model inversion error after a short initial period of learning. Furthermore, it is shown that the performance varies gracefully and monotonically improves as the adaptation gain parameter is increased. The e ect on control effort is modest, and is mainly perceptable only during a short training episode that can be associated with transition from hover to forward ight.

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