Design and Aerodynamic Modeling of MetaMorpher: A Hybrid Rotary andFixed-Wing Morphing UAV

This paper presents the design and control strategy for MetaMorpher, a metamorphic Unmanned Aerial Vehicle (UAV), capable of both spinning-wing hover and fixed flying-wing cruise flight. Since control of the cruise configuration is comparatively well established in the literature, this paper focuses on control of the MetaMorpher in hover mode, building on the flight dynamics model and conceptual design validated in previous work. The control algorithm is implemented using one of two phase-synchronized strategies: motor propulsion, which pulses motor thrust in synchrony with the vehicle's rotation, or flap propulsion, a novel strategy that pulses the deflection of the wing-mounted elevons. We evaluate both strategies in simulation through different flight experiments. Simulation results confirm the mathematical model and show stable reference tracking, demonstrating flap propulsion as a lightweight, decoupled alternative for hover control. Experimental testing validated the vertical-dynamics propulsion model against the physical prototype, demonstrating very good steady-state agreement across different configurations.

Co-design optimisation of autonomous systems has emerged as a powerful alternative to sequential approaches by jointly optimising physical design and control strategies. However, existing frameworks often neglect the robustness required for autonomous systems navigating unstructured, real-world environments. For agile Unmanned Aerial Vehicles (UAVs) operating at the edge of the flight envelope, this lack of robustness yields designs that are sensitive to perturbations and model mismatch. To address this, we propose a robust co-design framework for agile fixed-wing UAVs that integrates parametric uncertainty and wind disturbances directly into the concurrent optimisation process. Our bi-level approach optimises physical design in a high-level loop while discovering nominal solutions via a constrained trajectory planner and evaluating performance across a stochastic Monte Carlo ensemble using feedback LQR control. Validated across three agile flight missions, our strategy consistently outperforms deterministic baselines. The results demonstrate that our robust co-design strategy inherently tailors aerodynamic features, such as wing placement and aspect ratio, to achieve an optimal trade-off between mission performance and disturbance rejection.

The initial development phase of UAV swarms largely depends on simulation for experimental design and validation, yet existing open-source tools are often unmaintained, have steep learning curves, or are built around a single fixed scenario. The need for a comprehensive, modular simulation platform is a recognized research gap. This paper presents SwarmFly, a MATLAB-based simulation and test platform for multi- UAV swarms that addresses these gaps. SwarmFly combines a real-time operational map, four swarm coordination modes (leader-follower, decentralized, heterogeneous relay, and heterogeneous speed), simulated IMU telemetry, and IP-based geolocation with a plugin architecture that lets researchers add behaviors, fault models, and analysis tools without touching the core code. Eight bundled plugins extend the base simulator into a full test harness. The SwarmFly platform exposes multi-agent aerial swarms to a wide range of internal and external disruptions, enabling observation and quantification of underlying swarm control and behavioral mechanisms. This study verifies and characterizes each subsystem through eight experiments that measure formation accuracy, wind tolerance, fault recovery, energy endurance, and airspace compliance. The platform runs entirely in MATLAB. Its modular design supports straightforward extension toward hardware-in-the-loop testing, larger swarms, and higher-fidelity dynamics. An open-source release is available at [https://github.com/abhishekphadke/SwarmFly.git]