Nonlinear dynamics and the strong couplings that arise between multiple effectors undermine the assumptions behind conventional, linear control allocation techniques. When flight enters regimes where nonlinear effects dominate, linear allocators exhibit reduced accuracy due to increased model mismatch, which subsequently degrades performance and robustness of the flight control system. High fidelity onboard models and black box data driven approaches can recover accuracy across the flight envelope, but respectively impose computational burdens prohibitive for real time allocation and sacrifice the interpretability required for verification and fault diagnosis. This paper addresses these limitations by learning an explicit, physics constrained analytical model of the control effectiveness mapping from representative flight data using Sparse Identification of Nonlinear Dynamics. The resulting mapping is compact, interpretable, and admits analytical derivatives, enabling efficient computation within nonlinear solvers that additionally incorporate actuator dynamics, without requiring an onboard model. An online adaptation mechanism monitors prediction residuals and refreshes the model when significant plant changes are detected, providing graceful reconfiguration under actuator failures and varying operating conditions. The methodology is evaluated on a high fidelity nonlinear benchmark aircraft across a range of aggressive maneuvers, achieving accuracy comparable to a full nonlinear onboard model while substantially reducing computational cost relative to established baselines.

翻译：非线性动力学以及多执行器之间产生的强耦合作用，削弱了传统线性控制分配方法所依赖的基本假设。当飞行进入非线性效应主导的状态时，线性分配器因模型失配加剧而精度下降，进而降低飞行控制系统的性能与鲁棒性。高保真机载模型与黑箱数据驱动方法虽能在整个飞行包线内恢复精度，但前者会带来难以满足实时分配需求的计算负担，后者则牺牲了验证与故障诊断所需的可解释性。本文针对这些局限，通过稀疏辨识非线性动力学方法，从代表性飞行数据中学习一种显式且受物理约束的控制效能映射解析模型。所得映射模型紧凑、可解释，并具有解析导数形式，从而能够在无需机载模型的情况下，有效集成至同时考虑执行器动力学的非线性求解器中进行高效计算。在线自适应机制通过监测预测残差，在检测到被控对象发生显著变化时刷新模型，从而实现执行器故障及工况变化下的平稳重构。该方法在一架高保真非线性基准飞行器上，针对一系列剧烈机动进行了评估，其精度与完整非线性机载模型相当，同时相较于现有基准方法，计算成本显著降低。