Adaptive backstepping neural network control for three dimensions trajectory tracking of robotic airships

Abstract

The robotic airship provides a unique aerostatic platform for various applications, and these applications require high-precise trajectory tracking. However, it is a challenging problem due to nonlinearity and uncertainty of airship dynamics. This paper proposes an adaptive backstepping neural network control (ABNNC) approach to address this problem. First, the kinematics model and dynamics model of the robotic airship are presented. Second, the control problem of trajectory tracking is formulated, and a trajectory controller is designed using backstepping approach. A radial basis function neural network (RBFNN) is employed to approximate the uncertain dynamics model of the airship, and an adaptive law is designed to update the NN weight in the processing of approximation. The ultimate boundedness of the tracking errors are proven based on the Lyapunov theory. Finally, simulations are presented to illustrate the effectiveness and high precision of the designed controller.