Design of stability augmentor for aircraft nose wheel steering system based on Hopfield network identification algorithm

Characteristics of aircraft nose wheel steering were analyzed based on a two-freedom model. It is found that during a certain period of landing and taxing, the yaw rate command bandwidth rapidly decreases nearly to zero, causing the rudder pedal hypersensitiveness and steering characteristics deterioration. Then, a solution was proposed, that is, stability augmentation, in which yaw-rate feedback to nose wheel steering and parameters identification were used to adjust the bandwidth as desired. Hopfield neural network was used as the on-line parameters identification algorithm, and then the augment controller could be adjusted according to the result of identification in real-time operation, keeping the yaw rate command bandwidth within the desirable range. A Time Delay Unit was used to aid in avoiding controllers' possible awful effect in initial stage of parameters identification. Simulation results of a given-example illustrated that when the stability augmentor was applied, the yaw rate command bandwidth increased by approximately 15 times, while rudder pedal sensitivity was decreased by 6 orders of magnitude. Thus, the proposed stability augmentor could ameliorate the steering qualities greatly in the related period.