Model-Based Optimization of Transition Maneuvers of a Hybrid-Propulsion Compound-Helicopter UAV with Electrically Driven Rotors

The paper deals with the development of a model-based optimization framework for the design of power management strategies of a lightweight compound helicopter unmanned aerial vehicle (UAV) equipped with hybrid propulsion, with a focus on the transition maneuvers from cruise to hover. A high-fidelity multi-physics model of the hybrid propulsion system, composed of a combustion engine connected to a nose propeller for forward flight phases, and two permanent-magnet synchronous machines driving variable-pitch main and tail rotors to provide control during vertical flight phases, is firstly presented. In the model, the aerodynamic loads are obtained by applying an advanced code based on the blade element momentum theory (BEMT), including post-stall, tip-loss and low-Reynolds corrections, as well as induced velocities derived from the QPROP formulation by Drela. The dynamics of the electric machines is simulated via equivalent direct-quadrature models that take account of the field-oriented control technique used for the three-phase electronic commutation. As a first step of the dynamic optimization process, a so-called fixed-point optimization is obtained by finding the speeds and blade pitch angles of the rotors that minimize the steady-state electrical power absorption in vertical flight phases. To compare the optimization results with a baseline, a reference maneuver is then defined, in which the rotor speeds are ramped from standstill to the optimal fixed-point values, while the blade pitch angles are dynamically adjusted to ensure the UAV’s vertical equilibrium. Subsequently, a constrained optimization problem is solved on a closed-form reduction of the transition dynamics, determining the tilt law of the main rotor that minimizes the energy of the maneuver, and the optimum is verified on the high-fidelity model. Finally, the robustness of the system performance against parameter uncertainties is statistically characterized via Monte Carlo simulations. The results demonstrate the effectiveness of the proposed strategies in reducing the energy consumption and in improving the overall performance with respect to a baseline maneuver, highlighting the effectiveness of model-based optimization for the power management design of novel UAV platforms.