Uniaxial attitude control of uncrewed aerial vehicle with thrust vectoring under model variations by deep reinforcement learning and domain randomization

Abstract

Abstract The application of neural networks for nonlinear control has been actively studied in the field of aeronautics. Successful techniques have been demonstrated to achieve improved control performance in simulation using deep-reinforcement learning. To transfer the controller learnt in the simulation of real systems, domain randomization is an approach that encourages the adaptiveness of neural networks to changing environments through training with randomized parameters in environments. This approach applies to an extended context, with changing working environments, including model configurations. In previous studies, the adaptive performance of the domain-randomization-based controllers was studied in a comparative fashion over the model variations. To understand the practical applicability of this feature, further studies are necessary to quantitatively evaluate the learnt adaptiveness with respect to the training conditions. This study evaluates deep-reinforcement-learning and the domain-randomization-based controller, with a focus on its adaptive performance over the model variations. The model variations were designed to allow quantitative comparisons. The control performances were examined, with a specific highlight of whether the model variation ranges fell within or exceeded the randomization range in training.

Keywords

• Deep reinforcement learning
• UAV
• Control