Fixed-time convergence attitude control for a tilt trirotor unmanned aerial vehicle based on reinforcement learning

This paper presents a new nonlinear robust attitude control strategy for the tilt trirotor unmanned aerial vehicle (UAV). Fixed-time convergence control of the UAV's attitude tracking errors under the effects of model uncertainties and unknown external disturbances is achieved by utilizing the proposed control design. Actor-critic (AC) structure based neural networks are trained only with the information of the UAV's inputs and outputs data, to handle the UAV's modeling uncertainties with bounded estimation error. Then a sliding-mode based fixed-time controller is designed to compensate the approximation error of the neural networks and the unknown external disturbances. Based on the Lyapunov stability theory, the stability analysis of the closed-loop system is presented. The performance of the presented nonlinear robust control strategy is validated through the real-time flight experiments.