🤖 AI Summary
This paper addresses the joint optimization of thrust-module spatial configuration and controller design for rigidly coupled multi-UAV cooperative payload transportation, aiming to enhance flight precision and disturbance rejection robustness during payload carriage.
Method: We propose an H₂-norm-based robustness metric—first applied to configuration optimization—and develop a unified UAV–payload rigid-body dynamical model. Integrating hierarchical control architecture with optimal control theory, we formulate a co-design framework jointly optimizing module placement and control law. An iterative numerical optimization algorithm is designed to solve for the disturbance-rejection-optimal configuration.
Results: Experimental validation demonstrates that the optimized configuration significantly outperforms suboptimal alternatives in disturbance suppression, with measured performance closely matching theoretical predictions—confirming the efficacy and accuracy of the proposed co-design methodology.
📝 Abstract
We present a novel approach to cooperative aerial transportation through a team of drones, using optimal control theory and a hierarchical control strategy. We assume the drones are connected to the payload through rigid attachments, essentially transforming the whole system into a larger flying object with"thrust modules"at the attachment locations of the drones. We investigate the optimal arrangement of the thrust modules around the payload, so that the resulting system is robust to disturbances. We choose the $mathcal{H}_2$ norm as a measure of robustness, and propose an iterative optimization routine to compute the optimal layout of the vehicles around the object. We experimentally validate our approach using four drones and comparing the disturbance rejection performances achieved by two different layouts (the optimal one and a sub-optimal one), and observe that the results match our predictions.
