Neural Network Adaptive Control for Small Unmanned Tandem Helicopter

Abstract

Based on a small unmanned helicopter hovering ground testbed, considering strong dynamic couplings between rotors and body, the front rotor and the rear rotor of the small unmanned tandem helicopter, a nonlinear dynamic model of hovering small unmanned rotor helicopter was built by Newton law and Lagrange algorithm. A dynamic inversion method was employed to design the corresponding nonlinear flight control law. And a RBF neural network with on-line learning capability was designed to overcome the influences of exterior disturbance and uncertainty of modeling. Simulation results demonstrate that the instruction tracking behaviors are improved under constraints of desired requirements and the obtained results are verified