Event-Triggered Deep Learning Control of Quadrotors for Trajectory Tracking
Chaojie Zhu, Jicheng Chen, Makoto Iwasaki, Hui Zhang
Abstract
This article proposes an event-triggered deep learning control strategy to achieve real-time trajectory tracking control for quadrotors. In the training data collection phase, the event-triggered model predictive control (ETMPC) method is applied to the quadrotor in the simulation environment to generate training data. Then, a deep neural network (DNN) controller is trained to approximate the optimal control policy of the ETMPC. To further save computing resources of on-board processor, the event-triggered mechanism is incorporated with the DNN controller, and the dual-mode approach is employed in it. Finally, simulation and experimental results show that the proposed controller can ensure almost similar trajectory tracking performance to the ETMPC controller while requiring a lower control computation cost.