aiqbench analysis
This research applies Sparse Identification of Nonlinear Dynamics (SINDy), a machine learning technique, to solve the real-time control allocation problem in overactuated aircraft by deriving compact, interpretable physics-constrained models from flight data. The approach bridges the gap between high-fidelity simulation accuracy and low-latency execution requirements while maintaining the interpretability essential for safety-critical verification and fault diagnosis. By enabling efficient computation of analytical derivatives without heavy onboard models, this methodology advances autonomous control systems in regimes where traditional linear techniques fail due to nonlinear dynamics.
An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircrafts
arXiv:2606.13794v1 Announce Type: cross 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.
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.
Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Robotics (cs.RO)
arXiv:2606.13794 [eess.SY]
(or arXiv:2606.13794v1 [eess.SY] for this version)
https://doi.org/10.48550/arXiv.2606.13794 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 300 words
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