Neural networks-based command filtering control for a table-mount experimental helicopter

This paper presents neural networks based on command filtering control method for a table-mount experimental helicopter which has three rotational degrees-of-freedom. First, the controller is designed backstepping technique, and further technique used to solve derivative of virtual control, thereby avoiding effects signal noise. Secondly, model uncertainty helicopter’s system estimated by using networks. And then, Lyapunov stabilization analysis proves stability closed-loop attitude tracking system. Finally, experiment carried out clarify effectiveness proposed method.