Co-Design Optimisation of Morphing Topology and Control of Winged Drones

Abstract

The design and control of winged aircraft and drones is an iterative process aimed at identifying a compromise of mission-specific costs and constraints. When agility is required, shape-shifting (morphing) drones represent an efficient solution. However, morphing drones require the addition of actuated joints that increase the topology and control coupling, making the design process more complex. We propose a co-design optimisation method that assists the engineers by proposing a morphing drone's conceptual design that includes topology, actuation, morphing strategy, and controller parameters. The method consists of applying multi-objective constraint-based optimisation to a multi-body winged drone with trajectory optimisation to solve the motion intelligence problem under diverse flight mission requirements, such as energy consumption and mission completion time. We show that co-designed morphing drones outperform fixed-winged drones in terms of energy efficiency and mission time, suggesting that the proposed co-design method could be a useful addition to the aircraft engineering toolbox.

Figures (9)

• Figure 1: A co-designed morphing drone executing an agile manoeuvre.
• Figure 2: Morphing drone with its co-design parameters: $1\text{-}2)$ wing chord and span size; $3\text{-}4)$ wing vertical and horizontal location; $5)$ wing static orientations $R^{}_{0}$; $6)$ kinematic chain of the morphing mechanism; $7)$ servomotor models; $8)$ propulsion unit characteristics; and $9)$ controller weights.
• Figure 3: Multi-objective optimisation of drone morphology to reduce energy consumption and mission completion time in diverse flight environments. The multi-objective assessment (fitness) of each individual topology involves $n_s$ trajectory optimisations.
• Figure 4: Wing aerodynamic coefficients (airfoil naca0009, taper ratio $1.0$, aspect ratio $2.0$, ${R \space e} {=} 2{\times}10^5$). The blue dots are the flow5 data. The surfaces represent the analytical functions that fit data.
• Figure 5: Optimal Pareto front evaluated by the co-design optimisation method. Each line represents the output of a single run. The markers identify the individuals analysed in \ref{['fig:trajs_xy']}, \ref{['fig:trajs_speed_x']}, and \ref{['subse:results:codesign:validation']}. bix3 is a commercial fixed-wing drone later used for validation. The drones on the left side of the optimal Pareto fronts exhibit higher energy efficiency. Drones on the right bottom side show lower mission completion time.