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<p>This paper presents a novel real-time energy management strategy (EMS) for fuel cell/battery hybrid fixed-wing UAVs based on a chaotic augmentation fuzzy logic controller. In this framework, all fuzzy parameters and chaos suppression coefficients are optimized offline by the Fire Hawk Optimizer (FHO). The main innovation of this research is the simultaneous integration of three components that have never been integrated in the UAV literature: (i) Explicit modeling of environmental uncertainties—especially turbulent gusts—through a modified Lorenz system whose behavior is directly mapped to battery SOC and fuel cell hydrogen consumption dynamics; (ii) Real-time suppression of chaotic oscillations resulting from this model using a lightweight hybrid controller OGY + TDFC; and (iii) Single-stage global co-optimization of all tunable parameters (fuzzy membership functions, rule weights, OGY gain, TDFC gain and delay τ) via the recent FHO algorithm. Simulation results on a 5&#xa0;kg fixed-wing UAV under a realistic 3-hour mission with chaotic wind disturbances show that the proposed strategy achieves 44.35% reduction in fuel-cell energy consumption and extends endurance by more than one hour compared to a baseline FHO-optimized conventional fuzzy EMS (without chaos modeling). Compared to state-of-the-art methods reported in the literature, it provides 14.65–28.75% higher fuel savings than NMPC/AHEMS-based approaches, 24.35% higher savings over PSO-optimized fuzzy EMS, and significantly superior disturbance rejection without requiring accurate future mission preview or heavy online computation, while remaining executable in real-time (&lt; 16 ms/cycle) on typical UAV companion computers. These results highlight the effectiveness of explicit chaos-based uncertainty representation and lightweight hybrid chaos control in achieving superior fuel economy and robustness for long-endurance hybrid UAV operations.</p>

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Fuzzy energy management using a chaotic model to improve fuel consumption of fuel cell-battery hybrid fixed-wing UAVs operating under uncertainty control

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月14日

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  • Mohsen Rostami,
  • Payman Habibi,
  • Amirhamzeh Farajollahi

摘要

This paper presents a novel real-time energy management strategy (EMS) for fuel cell/battery hybrid fixed-wing UAVs based on a chaotic augmentation fuzzy logic controller. In this framework, all fuzzy parameters and chaos suppression coefficients are optimized offline by the Fire Hawk Optimizer (FHO). The main innovation of this research is the simultaneous integration of three components that have never been integrated in the UAV literature: (i) Explicit modeling of environmental uncertainties—especially turbulent gusts—through a modified Lorenz system whose behavior is directly mapped to battery SOC and fuel cell hydrogen consumption dynamics; (ii) Real-time suppression of chaotic oscillations resulting from this model using a lightweight hybrid controller OGY + TDFC; and (iii) Single-stage global co-optimization of all tunable parameters (fuzzy membership functions, rule weights, OGY gain, TDFC gain and delay τ) via the recent FHO algorithm. Simulation results on a 5 kg fixed-wing UAV under a realistic 3-hour mission with chaotic wind disturbances show that the proposed strategy achieves 44.35% reduction in fuel-cell energy consumption and extends endurance by more than one hour compared to a baseline FHO-optimized conventional fuzzy EMS (without chaos modeling). Compared to state-of-the-art methods reported in the literature, it provides 14.65–28.75% higher fuel savings than NMPC/AHEMS-based approaches, 24.35% higher savings over PSO-optimized fuzzy EMS, and significantly superior disturbance rejection without requiring accurate future mission preview or heavy online computation, while remaining executable in real-time (< 16 ms/cycle) on typical UAV companion computers. These results highlight the effectiveness of explicit chaos-based uncertainty representation and lightweight hybrid chaos control in achieving superior fuel economy and robustness for long-endurance hybrid UAV operations.