AI & ML
Zonotope-Based Elastic Tube Model Predictive Control
arXiv cs.SY0 views
arXiv:2509.19824v3 Announce Type: replace Abstract: Tube-based Model Predictive Control (MPC) is a widely adopted robust control framework for constrained linear systems under additive disturbance. The paper is focused on reducing the numerical complexity associated with the tube parameterization, described as a sequence of elastically-scaled zonotopic sets. A new class of scaled-zonotope inclusion conditions is proposed, alleviating the need for a priori specification of certain set-containment constraints and achieving significant reductions in complexity. A comprehensive complexity analysis is provided for both the polyhedral and the zonotopic setting, illustrating the trade-off between an enlarged domain of attraction and the required computational effort. The proposed approach is validated through extensive numerical experiments.
Source:https://arxiv.org/abs/2509.19824PreviousClinical-Protocol-Aware Cyber Resilience for Connected Clinical Technologies Under AI-Enabled Adversarial ConditionsNextBiased Stochastic Gradient Descent under Local Geometry, with Application to Data-Driven LQR
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