Asynchronous Sensitivity-Based Distributed NMPC
Abstract
This paper presents a cooperative distributed model predictive control (MPC) scheme for nonlinear continuous-time systems. The centralized optimal control problem is solved asynchronously via a fixed number of sensitivity-based distributed programming (SBDP) iterations. The proposed scheme requires only neighbor-to-neighbor communication and no synchronization between agents during optimization. Under nominal MPC stability and bounded information delay, local exponential stability is established for a sufficiently large number of per-agent SBDP iterations. Numerical and hardware-in-the-loop results on both Ethernet and Wi-Fi demonstrate the benefits of an asynchronous execution, reducing execution times by over 60% while maintaining comparable closed loop performance.
Cite
@article{arxiv.2607.03412,
title = {Asynchronous Sensitivity-Based Distributed NMPC},
author = {Maximilian Pierer von Esch and Andres Völz and Knut Graichen},
journal= {arXiv preprint arXiv:2607.03412},
year = {2026}
}