Soft Multicopter Control Using Neural Dynamics Identification

Abstract

We propose a data-driven method to automatically generate feedback controllers for soft multicopters featuring deformable materials, non-conventional geometries, and asymmetric rotor layouts, to deliver compliant deformation and agile locomotion. Our approach coordinates two sub-systems: a physics-inspired network ensemble that simulates the soft drone dynamics and a custom LQR control loop enhanced by a novel online-relinearization scheme to control the neural dynamics. Harnessing the insights from deformation mechanics, we design a decomposed state formulation whose modularity and compactness facilitate the dynamics learning while its measurability readies it for real-world adaptation. Our method is painless to implement, and requires only conventional, low-cost gadgets for fabrication. In a high-fidelity simulation environment, we demonstrate the efficacy of our approach by controlling a variety of customized soft multicopters to perform hovering, target reaching, velocity tracking, and active deformation.