Attitude Control of a Moving Mass–Actuated UAV Based on Deep Reinforcement Learning
Xiaoqi Qiu, Changsheng Gao, Kefan Wang, Wuxing Jing
Abstract
A moving mass–actuated unmanned aerial vehicle (MAUAV) is controlled by mass sliders installed inside the airframe and has the advantages of high aerodynamic efficiency and good stealth performance. However, designing a controller for it faces severe challenges due to the strong nonlinearity and coupling of its dynamics. To this end, we proposed an attitude controller based on deep reinforcement learning for the MAUAV. It directly maps the states to the needed deflection of the actuators and is an end-to-end controller. For the sparse reward problem, the reward function required for training is reasonably designed through reward shaping to hasten the algorithm’s training speed. In training, random initialization and parameter perturbation are used to strengthen the final policy’s robustness further. The simulation results tentatively demonstrate that the proposed controller is not only robust but suboptimal. Compared with an active disturbance rejection controller (ADRC) optimized by the particle swarm algorithm, our controller still guarantees a 100% success rate in multiple unlearned scenarios, meaning it has good generalization ability.