Formation Control of Multi-quadrotors Based on Deep Q-learning
Roujin Mousavifard, Khalil Alipour, Mohammad Amin Najafqolian, Payam Zarafshan · 2022 10th RSI International Conference on Robotics and Mechatronics (ICRoM) · 2022
The purpose of this study is to address a model-free formation problem for a team of quadrotors. A cascade controller including a tracking controller and an attitude controller, is developed. The assumptions preserve the nonlinearity and the under-actuation of the model. The tracking controller uses reinforcement learning to develop a model-free online controller. Moreover, the attitude controller is equipped with an actor-critic neural network to solve the nonlinearity issue. The whole formation leads with a virtual leader in the center of the predesigned formation. Simulation results of multiaerial vehicles including four heterogeneous quadrotors, demonstrate the effectiveness of the proposed controller.