An Efficient Differentiable Model Predictive Control for UAV Attitude Regulation

Abstract

Data-driven learning optimization, which considers optimization as a means to perform end-to-end learning, is an emerging methodology used to solve large-scale learning and continuous control tasks. These methods provide mathematically tractable solutions with inherent interpretability, but their training can be inefficient due to the need to differentiate through the optimization problem and to solve gradient information in every training iteration. To address this issue, we propose a novel learning algorithm called Efficient Differentiable Model Predictive Control (EDMPC), which significantly improves computational efficiency by integrating an optimization-driven inference step within the backpropagation process. More concretely, we derive an analytical gradient of the trajectory from the optimal problem relative to the tunable parameter, enabling significant acceleration of the end-to-end learning procedure. This gradient can be solved by a standard optimizer in a recursive form, which then provides the direction for parameter updates. An unmanned aerial vehicle control task was conducted to demonstrate the effectiveness of EDMPC in controlling complex dynamic systems. The results indicate that the proposed EDMPC method exhibits superior efficiency in the learning process and accuracy in the control process.