Compensation function observer based adaptive command-filtered attitude control for quadrotor unmanned aerial vehicles

This article introduces an innovative adaptive control strategy for precise attitude tracking control of Quadrotor Unmanned Aerial Vehicles (QUAVs), addressing critical challenges posed by state constraints, model uncertainties, and external disturbances. Departing from conventional extended state observers, a Compensation Function Observer (CFO) is established to compensate for the lumped disturbances through fully exploiting system information. Subsequently, by integrating the command filter and Barrier Lyapunov Function (BLF), an adaptive backstepping control strategy is proposed, which ensures rigorous boundedness of tracking errors, without violation of state constraints. Finally, comprehensive simulations are conducted to illustrate the advantages of the approach proposed.