An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircrafts

• URL: http://arxiv.org/abs/2606.13794v2
• Date: Thu, 18 Jun 2026 11:10:16 GMT
• Title: An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircrafts
• Abstract summary: nonlinear dynamics and the strong couplings that arise between multiple effectors undermine the assumptions behind linear control allocation techniques.<n>This paper addresses these limitations by learning an explicit, constrained analytical model of the control effectiveness mapping from representative flight data.<n>The resulting mapping is compact, interpretable, and admits analytical derivatives, enabling efficient computation within solvers.
• Score: 2.2940141855172036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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