Sensitivity-Based Tube NMPC for Cooperative Aerial Structures Under Parametric Uncertainty

TL;DR

This paper introduces a sensitivity-based tube NMPC method for cooperative aerial chains that enhances robustness to parametric uncertainties while maintaining high tracking performance.

Contribution

The paper develops a novel sensitivity-based tube NMPC framework for aerial chains, incorporating online constraint tightening under parametric uncertainty.

Findings

Improved constraint margins under uncertainty.

Tracking performance comparable to nominal NMPC.

Effective robustness to link mass, length, and inertia variations.

Abstract

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.