Design of Neural Network-based Backstepping Controller for the Folding-Boom Aerial Platform Vehicle

In this paper, the robust trajectory tracking problem is addressed for the work platform of folding-boom aerial platform vehicle in the presence of uncertainties and disturbances. The control objective is to make the work platform move along a desired reference trajectory and make the vibration inhibit at the same time. Since neural network system can approximate any nonlinear function with arbitrary accuracy over a compact set in the light of the universal approximation theorem, a neural network-based backstepping controller, which composed of backstepping control and neural network, is proposed for the trajectory tracking control of the work platform in the case of modeling uncertainties and disturbances. According to Lyapunov stability theorem, the stability and convergence of the overall system can be guaranteed by the derived control law. In addition, simulation results demonstrate that the proposed controller is effective for suppressing the vibration and reducing trajectory tracking error of the work platform.