Sensitivity-Based Tube NMPC for Cooperative Aerial Structures Under Parametric Uncertainty
For researchers in aerial robotics, it provides a robust control method for cooperative aerial structures under parametric uncertainty, though it is an incremental extension of existing tube NMPC techniques.
This paper develops a sensitivity-based tube NMPC for cooperative aerial chains under bounded parametric uncertainty, achieving improved constraint satisfaction with tracking performance comparable to nominal NMPC.
This paper presents a sensitivity-based tube Nonlinear Model Predictive Control (NMPC) framework for cooperative aerial chains under bounded parametric uncertainty. We consider a planar two-vehicle chain connected by rigid links, modeled with input-rate actuation to enforce slew-rate and magnitude limits on thrust and torque. Robustness to uncertainty in link mass, length, and inertia is achieved by propagating first-order parametric state sensitivities along the horizon and using them to compute online constraint-tightening margins. We robustify an inter-link separation constraint, implemented via a smooth cosine embedding, and thrust-magnitude bounds. The method is implemented in MATLAB and evaluated with boundary-hugging maneuvers and Monte-Carlo uncertainty sampling. Results show improved constraint margins under uncertainty with tracking performance comparable to nominal NMPC.