Decentralized Model Predictive Control for Equilibrium-based Collaborative UAV Bar Transportation

In this paper we analyze the equilibrium points of a collaborative transportation task, composed of two unmanned aerial vehicles and a payload - in this case, a bar. Moreover, centralized and decentralized linear model predictive controllers are designed, where the nonlinear dynamics are linearized around the equilibrium points previously analyzed. A comparison between the centralized and decentralized formulations is provided, based on experimental results for both setups, and considering the time to solution and performance of each controller. Our findings provide new operational equilibrium points that can be paired with predictive model-based controllers for efficient operation.

Automated summary

This paper analyzes the equilibrium points of a collaborative transportation task with two unmanned aerial vehicles and a payload. Centralized and decentralized linear model predictive controllers are designed based on linearizing the nonlinear dynamics around the previously analyzed equilibrium points. A comparison between the centralized and decentralized formulations is provided, considering the time to solution and performance of each controller. The findings offer new operational equilibrium points for efficient operation with predictive model-based controllers.