Sensitivity-Based Tube NMPC for Cooperative Aerial Structures Under Parametric Uncertainty
Giuseppe Silano, Quentin Sablé, Marco Tognon, Luigi Iannelli, Antonio Franchi
TLDR
This paper introduces a sensitivity-based tube NMPC for cooperative aerial chains, robustly handling parametric uncertainty with improved constraint margins.
Key contributions
- Develops a sensitivity-based tube NMPC for cooperative aerial chains with input-rate actuation.
- Propagates first-order state sensitivities to robustly handle parametric uncertainty in link properties.
- Computes online constraint-tightening margins for inter-link separation and thrust magnitude bounds.
- Shows improved constraint margins under uncertainty while maintaining nominal NMPC tracking performance.
Why it matters
This paper offers a robust control solution for multi-robot systems operating under uncertain conditions, crucial for real-world applications. Its method of using sensitivity analysis to tighten constraints online enhances safety and reliability in complex aerial maneuvers.
Original 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.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.