Adaptive tracking control based on neural approximation for the yaw motion of a small-scale unmanned helicopter

This article aims to study a solution that can solve the problem of tracking control for yaw motion of an unmanned helicopter. The non-affine nonlinear equation is converted to a simplified affine model. The unknown parameters are estimated by the Levenberg–Marquardt algorithm. An autonomous flight controller is developed with the Lyapunov-based adaptive controller for a discrete-time system. For flight data collection and verification purpose, the software-in-the-loop is constructed based on Simulink and X-Plane simulator. The designed system is applied in the control of the yaw motion of an R30 V2 helicopter under ideal and turbulent environments. The performance of the proposed method is compared with the fuzzy logic controller, and the simulation results show that the quality of the current approach is considerably better.