arXiv:2606. 16171v1 Announce Type: cross Abstract: Multi-fuel compression ignition (CI) engines offer superior power density and fuel flexibility.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 18393v1 Announce Type: cross Abstract: Multi-fuel compression-ignition engines offer fuel flexibility but introduce uncertain, time-varying fuel reactivity, represented by cetane number (CN), which complicates cycle-to-cycle combustion-phasing control.
The paper introduces a robust and adaptive model predictive control framework for uncertain nonlinear systems with bounded disturbances and unmodeled nonlinearities, leveraging Gaussian Processes to learn dynamics from noisy measurements. It derives robust predictions for GP models using contraction metrics, integrating them into the MPC formulation to ensure recursive feasibility, robust constraint satisfaction, and convergence to a reference state with high probability. A numerical example involving a planar quadrotor experiencing challenging ground effects demonstrates significant performance gains from the robust prediction method and online learning.
arXiv:2604. 01730v2 Announce Type: replace Abstract: This paper investigates Koopman operator-based approaches for multivariable control of a two-spool turbofan engine.
The paper introduces Continual Uncertainty Learning (CUL), a curriculum-based continual learning framework that decomposes robust control of nonlinear systems with multiple heterogeneous uncertainties into a sequence of tasks. By progressively expanding and diversifying plant uncertainties and applying memory-efficient anti-forgetting regularization, CUL enables a policy to acquire strategies for each uncertainty sequentially while a model-based controller provides a shared baseline performance. Applied to an active vibration controller for automotive powertrains, the approach demonstrates robustness to structural nonlinearities and dynamic variations, improving control performance and sample efficiency.
arXiv:2603. 16481v3 Announce Type: replace Abstract: Non-conservative uncertainty bounds are essential for making reliable predictions about latent functions from noisy data, and thus, a key enabler for safe learning-based control.
arXiv:2609.36712v1 Announce Type: cross Abstract: Accurately learning nonlinear dynamics from a finite-duration experiment requires the efficient collection of informative data. We address this chall...