Contraction-based Neural Control for Cooperative Aerial Payload Transportation with Variable-length Cables

Abstract: This paper presents a novel neural nonlinear control framework for a multi-drone slung payload system with variable-length cables and a rigid-body payload. The equations of motion are formulated into a decoupled structure, where the payload and cable length dynamics are governed by independent control channels, facilitating modularized controller design on reduced-order subsystems. A neural control contraction metric (CCM) controller and a neural feedback controller are jointly trained to enforce contraction conditions for the payload subsystem. Separately, a cable length control law is derived that exploits the variable-length degree of freedom for obstacle avoidance. Numerical simulations demonstrate trajectory tracking of a rigid-body payload and gate traversal capabilities of the overall system under the proposed control framework.