Event-Triggered Learning-Based Control of Quadrotors for Accurate Agile Trajectory Tracking

Accurate tracking control of agile quadrotors is challenging because of complicated aerodynamic effects. In this letter, we develop a tunable event-triggered learning-based control framework for quadrotors to achieve agile trajectory tracking in the presence of aerodynamic effects. A novel Gaussian process (GP)-augmented differential flatness-based controller is proposed to produce collective thrusts and body rates (CTBR) control commands to perform agile trajectory tracking. To achieve a better trade-off between the computational burden of GP learning and the tracking accuracy, a tunable event-triggered learning strategy is designed by using the Lyapunov redesign technique. We verify that the developed framework can effectively improve the performance of agile trajectory tracking of quadrotors and release the computational burden of GP learning in the presence of aerodynamic effects through both simulations and real-world experiments. Experimental results show that our framework can reduce the computational burden by 78.4% while maintaining satisfiable tracking accuracy.