Minimal Parameter Estimation Based Non-Linear Control of Hexarotors With Uncertainty in Mass

Dynamics of a hexarotor contain non-linearities and uncertainties in the system parameters. The purpose of this study is to design a self-learning controller to track the altitude commands given to a hexarotor and achieve a stable hovering position amidst uncertainties in mass due to changes in the payload. The backstepping controller is a Lyapunov based controller which can accurately control and stabilize the vehicle when plant dynamics are completely known. While adaptive backstepping controller employs a dynamic feedback law with which it adapts to changes in the physical parameters. In this work, a Lyapunov based backstepping control law is first developed for a hexarotor platform, later an adaptive backstepping control is also introduced to nullify any destabilizing uncertainties in mass and thereby achieve stable hovering. Numerical simulations are performed on the MATLAB platform with variation in mass incorporated to test the effectiveness of the proposed control strategy. Software implementation of the system gives satisfactory results.