Robust state and output feedback prescribed performance interval type-3 fuzzy reinforcement learning controller for an unmanned aerial vehicle with actuator saturation

This paper presents a novel adaptive reinforcement learning control method with interval type-3 fuzzy neural networks to improve the trajectory tracking control performance of quadrotor unmanned aerial vehicles in challenging flight conditions. The proposed reinforcement learning controller is independent of the system's dynamics, and only relies on measurable signals of the system. An adaptive robust controller in collaboration with the suggested reinforcement learning method is designed to significantly improve the robustness of the control system. The maximum overshoot/undershoot, convergence rate and final tracking accuracy are ensured a priori by the prescribed performance control methodology. To develop the proposed controller and to achieve a high-performance closed-loop system, a high-gain observer is employed in order to estimate the velocity and acceleration of the quadrotor unmanned aerial vehicles system. The uniform ultimate boundedness stability of the proposed control algorithm is achieved by a Lyapunov-based stability analysis. Finally, in the simulation section, it is shown that the presented intelligent controller with the proposed learning algorithm result in a better performance in contrast to the other kind of conventional control techniques.

Automated summary

This paper presents a novel adaptive reinforcement learning control method with interval type-3 fuzzy neural networks to improve the trajectory tracking control performance of quadrotor unmanned aerial vehicles in challenging flight conditions. The proposed controller is independent of system dynamics and only relies on measurable signals. An adaptive robust controller in collaboration with reinforcement learning significantly improves system robustness. Prescribed performance control methodology ensures predefined overshoot/undershoot, convergence rate, and final tracking accuracy. High-gain observer is employed to estimate quadrotor velocity and acceleration. Lyapunov-based stability analysis achieves uniform ultimate boundedness stability. The simulation section demonstrates better performance of the proposed intelligent controller with the learning algorithm compared to conventional techniques.