Nonlinear Adaptive Neural Controller for Unstable Aircraft

A model reference indirect adaptive neural control scheme that uses both off-line and online learning strategies is proposed for an unstable nonlinear, aircraft controller design. The bounded-input-bounded-output stability requirement for the controller design is circumvented using an off-line, finite interval of time training scheme. The aircraft model is first identified using a neural network with linear filter (also known as time-delayed neural network) with the available input-output data for a finite time interval. The finite time interval is selected such that this time interval is less than the critical time interval for the aircraft from its stability point of view (similar to the time to double). A procedure to select this critical time interval is also presented. For a given reference model and the identified model, the controller neural-network weights are adapted off-line for the same time interval. The off-line trained neural controller ensures the stability and provides the necessary tracking performance for the unstable aircraft. If there is a change in the aircraft dynamics or characteristics, the trained neural identifier and controller are also adapted online. The theoretical results are validated using the simulation studies based on a locally nonlinear longitudinal high-performance fighter aircraft similar to the F-16. The neural controller design proposed is also compared with the feedback error learning neural control strategy in terms of the tracking ability and control efforts for various level flight conditions and fault conditions such as modeling uncertainties and partial control surface loss. We also present the robustness of the aircraft under extreme wind and noise conditions.