Publication: Metaheuristic-driven control optimization for hybrid quadplane UAV in multimodal flight operations.
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Date
2026-08
Authors
Wang, Shirui
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Abstract
Hybrid quadplane unmanned aerial vehicles (UAVs), which combine the vertical takeoff and landing capability of rotary-wing platforms with the long-range and high-efficiency cruise performance of fixed-wing aircraft, have attracted increasing attention for complex and endurance-critical missions. However, reliable autonomous operation across rotary-wing, fixed-wing, and transition flight regimes remains challenging. Existing studies still exhibit limitations in three main aspects: multimodal dynamic modeling often struggles to simultaneously achieve high modeling accuracy and computational efficiency; transition control strategies mainly depend on direct controller switching or simplified allocation methods, which may cause discontinuous responses; and metaheuristic-based controller optimization remains vulnerable to premature convergence and insufficient population diversity. To address these challenges, this thesis proposes a unified framework combining multimodal dynamic modeling,mode-dependent control strategy design, and enhanced metaheuristic-driven controller
optimization for hybrid quadplane UAVs. First, flight-test-based multimodal dynamic models are developed for rotary-wing, transition, and fixed-wing regimes using system
identification techniques. The proposed approach provides experimentally validated dynamic models with improved accuracy and practical computational efficiency. Based on these models, dedicated controllers are designed for different flight modes, including cascaded PID control for rotary-wing flight, PID-TECS control for fixed-wing flight,
and an airspeed-based control-weight allocation strategy for transition flight. Simulation results demonstrate that the identified models achieve model–data matching rates exceeding 84% across all flight regimes, while the proposed transition strategy effectively eliminates the abrupt attitude change caused by mode switching. Second,an enhanced metaheuristic optimization algorithm, termed the Eagle Hawk–Artificial Rabbit Optimization algorithm, is proposed to improve controller parameter tuning. EHARO introduces an Eagle Hawk Threat-Based Perturbation Mechanism and a self centric exploration strategy to enhance population diversity and global search capability. Evaluations using 29 benchmark functions from the CEC2017 test suite demonstrate that EHARO achieves superior optimization performance compared with several state of-the-art algorithms. When applied to hybrid quadplane attitude control, EHARO optimized PID controllers achieve faster dynamic responses and improved attitude stabilization across roll, pitch, and yaw channels. In particular, the roll-channel settling time is reduced by more than 8.2%, while reductions exceeding 52% and 75% are achieved in the yaw channel compared with IARO and ARO, respectively. Overall,
this research establishes a unified modeling, control, and optimization framework that provides an effective approach for improving modeling accuracy, transition stability, and controller performance in hybrid quadplane UAVs operating in multimodal flight environments.