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CEAS EuroGNC 2026 |
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Safe, Hybrid Control Allocation for the Innovative Control Effectors (ICE) Aircraft |
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| Abstract: This study presents a safe, frequency-separated hybrid control-allocation approach that combines a fast convex quadratic program (QP) with a slow reinforcement-learning (RL) guidance-layer. The fast allocation runs frame-wise at high rates, solving a strictly convex control allocation optimization problem via quadratic programming with equality slack, position and rate constraints. The slow layer is moment invariant, dynamically manipulating weights to redistribute control effort within the null space of the local control effectiveness. The Innovative Control Effectors (ICE) aircraft is used as the testing framework for the proposed approach. Results show that this allocation architecture can reduce cumulative control allocation error over a period and better distribute commands across available control effectors without injecting unintended moments into system. | ||||
| Keywords: Dynamic control allocation; Reinforcement learning; Quadratic programming; Over-actuated aircraft; Null space; Hybrid allocation; Innovative Control Effectors (ICE) | ||||
 View PDF CEAS-GNC-2026-033 doi: 10.82124/CEAS-GNC-2026-033 |
| Hasan Isci, Emre Koyuncu: Safe, Hybrid Control Allocation for the Innovative Control Effectors (ICE) Aircraft. Proceedings of the 2026 CEAS EuroGNC conference. Madrid, Spain. May 2026. doi: 10.82124/CEAS-GNC-2026-033. |
| BibTeX entry (UTF-8): @Incollection{CEAS-GNC-2026-033, author = {Isci, Hasan and Koyuncu, Emre}, title = {Safe, Hybrid Control Allocation for the Innovative Control Effectors (ICE) Aircraft}, booktitle = {Proceedings of the 2026 {CEAS EuroGNC} conference}, address = {Madrid, Spain}, month = may, year = {2026}, doi = {10.82124/CEAS-GNC-2026-033} } |