{"ID":5935695,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T02:10:06.972658124Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03412","arxiv_id":"2607.03412","title":"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.","short_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-nei...","url_abs":"https://arxiv.org/abs/2607.03412","url_pdf":"https://arxiv.org/pdf/2607.03412v1","authors":"[\"Maximilian Pierer von Esch\",\"Andres Völz\",\"Knut Graichen\"]","published":"2026-07-03T15:20:58Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}