命令式MPC：一种用于无人机姿态控制、基于可微MPC的端到端自监督学习方法
Imperative MPC: An End-to-End Self-Supervised Learning with Differentiable MPC for UAV Attitude Control
AI总结：
针对无人机姿态控制，提出结合惯性里程计和可微模型预测控制的自监督端到端框架，通过双层优化协同提升IMU预测与MPC参数学习，在强风下表现有效。
摘要：
非线性动力学的建模与控制在机器人领域至关重要，尤其是在存在不可预测外部影响和复杂动力学的场景中。传统的级联模块化控制流水线通常因保守的假设和繁琐的参数调整而导致次优性能。纯数据驱动方法有望实现稳健的性能，但存在样本效率低、仿真到现实差距以及依赖庞大数据集的问题。以端到端方式结合基于学习的方法和传统基于模型控制方法的混合方法提供了一种有前景的替代方案。本工作提出了一种自监督学习框架，结合了基于学习的惯性里程计（IO）模块和可微模型预测控制（d-MPC），用于无人机（UAV）姿态控制。IO对原始IMU测量值进行去噪并预测UAV姿态，随后由MPC在双层优化（BLO）设置下对其进行优化以生成控制动作，其中内层MPC优化控制动作，外层则最小化真实世界与预测性能之间的差异。因此，该框架是端到端的，并且可以以自监督方式进行训练。该方法结合了基于学习的感知能力与可解释的基于模型的控制。结果显示，即使在强风环境下也具有有效性。它能够同时增强MPC参数学习和IMU预测性能。
英文摘要：
Modeling and control of nonlinear dynamics are critical in robotics, especially in scenarios with unpredictable external influences and complex dynamics. Traditional cascaded modular control pipelines often yield suboptimal performance due to conservative assumptions and tedious parameter tuning. Pure data-driven approaches promise robust performance but suffer from low sample efficiency, sim-to-real gaps, and reliance on extensive datasets. Hybrid methods combining learning-based and traditional model-based control in an end-to-end manner offer a promising alternative. This work presents a self-supervised learning framework combining learning-based inertial odometry (IO) module and differentiable model predictive control (d-MPC) for Unmanned Aerial Vehicle (UAV) attitude control. The IO denoises raw IMU measurements and predicts UAV attitudes, which are then optimized by MPC for control actions in a bi-level optimization (BLO) setup, where the inner MPC optimizes control actions and the upper level minimizes discrepancy between real-world and predicted performance. The framework is thus end-to-end and can be trained in a self-supervised manner. This approach combines the strength of learning-based perception with the interpretable model-based control. Results show the effectiveness even under strong wind. It can simultaneously enhance both the MPC parameter learning and IMU prediction performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
