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. • Reinforcement learning networks are designed to compensate modeling uncertainties. • A fixed-time controller is proposed for approximation errors and disturbances. • Completed stability analysis of the closed-loop system is presented. • Real-time experiments are performed to validate the performance of the control law. [ABSTRACT FROM AUTHOR]