发表机构
University of Bechar（贝沙尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出混合PEM-GP框架，结合物理建模与数据驱动学习，为四旋翼飞行器系统辨识提供兼具高精度与校准不确定性估计的模型，支持不确定性感知决策。
AI 中文摘要
精确的动力学模型对实现四旋翼飞行器的可靠控制至关重要。经典系统辨识方法因可解释性强而被广泛使用，但往往无法捕捉重要的非线性效应，这种效应在小型空中平台上更为明显。数据驱动方法能更有效地表示复杂的非线性动力学，却以可解释性降低和缺乏校准的不确定性估计为代价。本研究提出一种结合基于物理的建模与数据驱动学习、同时明确考虑不确定性的框架：首先使用预测误差法（Prediction Error Method, PEM）辨识基于物理的模型，以捕捉系统的主要结构；再用高斯过程（Gaussian Process, GP）对剩余动力学建模，直接从数据中学习残差行为。这种分离可区分已知物理效应与未建模动力学。在类Duckiedrone的实验装置上验证该框架，结果显示PEM-GP模型的预测精度与长短期记忆网络（Long Short-Term Memory, LSTM）相当，还能提供校准的不确定性估计，这种组合提升了模型可靠性，支持不确定性感知决策。
英文摘要
Accurate dynamic models play a central role in achieving reliable control of quadcopters. Classical system identification methods remain widely used, mainly because of their interpretability. However, they often fail to capture important nonlinear effects, especially in small-scale aerial platforms where such effects become more pronounced. Data-driven approaches offer a different perspective. They can represent complex nonlinear dynamics more effectively, but this comes at the cost of reduced interpretability and the absence of well-calibrated uncertainty estimates. In this work, we propose a framework that combines physics-based modeling with data-driven learning, while explicitly accounting for uncertainty. A physics-based model is first identified using the Prediction Error Method (PEM), which captures the main structure of the system. The remaining dynamics are then modeled using a Gaussian Process (GP), allowing the residual behavior to be learned directly from data. This separation makes it possible to distinguish between known physical effects and unmodeled dynamics. The proposed framework is validated on a Duckiedrone-like experimental setup. The results show that the PEM-GP model achieves prediction accuracy comparable to that of a Long Short-Term Memory (LSTM) network, while additionally providing calibrated uncertainty estimates. This combination improves model reliability and supports uncertainty-aware decision-making.
Comments19 pages, 7 figures. Published in International Journal of Control, Automation, and Systems, Vol. 24, No. 8, pp. 2047-2057, 2026
Journal refInternational Journal of Control, Automation, and Systems, Vol. 24, No. 8, pp. 2047-2057, 2026
DOI：10.1007/s12555-026-00123-5
