用能量约束提升神经模型对飞行机器人的控制精度与稳定性。
Energy-based Regularization for Learning Residual Dynamics in Neural MPC for Omnidirectional Aerial Robots
- 在神经模型中引入能量正则化损失，让其学习残差动力学时保持系统能量稳定。
- 实测使位置平均绝对误差降低15%，比解析模型改善23%。
- 适合需要高稳定性飞行控制的无人机研究者和工程师参考。
数据驱动的模型预测控制（MPC）已成为控制理论的核心研究方向。将最优控制框架与深度学习结合，可在无需复杂解析模型的情况下精确跟踪控制任务。然而，系统动力学往往微妙，神经模型难以理解惯性、能量守恒等物理特性。本文提出一种新型能量基正则化损失函数，用于训练学习全向飞行机器人残差动力学的神经模型。该正则化促使神经网络产生的控制修正能稳定系统能量。残差动力学被整合进MPC框架，在三项真实实验中，位置平均绝对误差（MAE）相比解析MPC降低23%。与无正则化的标准神经MPC相比，本方法在未显式优化稳定性的情况下显著提升飞行稳定性，且最大可降低15%的MAE。代码已公开：https://github.com/johanneskbl/jsk_aerial_robot/tree/develop/neural_MPC。
原文摘要 · Abstract (English)
Data-driven Model Predictive Control (MPC) has lately been the core research subject in the field of control theory. The combination of an optimal control framework with deep learning paradigms opens up the possibility to accurately track control tasks without the need for complex analytical models. However, the system dynamics are often nuanced and the neural model lacks the potential to understand physical properties such as inertia and conservation of energy. In this work, we propose a novel energy-based regularization loss function which is applied to the training of a neural model that learns the residual dynamics of an omnidirectional aerial robot. Our energy-based regularization encourages the neural network to cause control corrections that stabilize the energy of the system. The residual dynamics are integrated into the MPC framework and improve the positional mean absolute error (MAE) over three real-world experiments by 23% compared to an analytical MPC. We also compare our method to a standard neural MPC implementation without regularization and primarily achieve a significantly increased flight stability implicitly due to the energy regularization and up to 15% lower MAE. Our code is available under: https://github.com/johanneskbl/jsk_aerial_robot/tree/develop/neural_MPC.
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