Abstract: Nonlinear model predictive control (NMPC) algorithms have been widely used in autonomous vehicle trajectory tracking, yet their performance is primarily limited by the accuracy of the ...
Anyone with a chronic illness understands the struggle of living with a disease that is deeply unpredictable. Many such ...
Abstract: In this paper, we propose a robust model predictive control framework-disturbance-observer-based homothetic tube model predictive control (D-HTMPC)-for perturbed discrete-time input-affine ...
A Monte Carlo and decision-tree risk analysis of a U.S. plan to seize Kharg Island and its impact on oil prices, war risk, ...
Predictive risk scores created using administrative claims and publicly available social determinants of health data strongly ...
The spatio-temporal evolution of wall-bounded turbulence is characterized by high nonlinearity, multi-scale dynamics, and ...
The Kolmogorov-Arnold Network (abbr. KAN) is a novel neural network architecture inspired by the Kolmogorov-Arnold ...
Ambient temperature is a key environmental driver of cardiovascular health. With rising global temperatures and increasing frequency, intensity, and duration of extreme temperature events, ...
Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...
U.S.–Israel–GCC strategy, Hormuz risk, oil shock, global supply chain impact and escalation analysis. As of March 27, 2026—one month after the February 28 assassination of Supreme Leader Ayatollah Ali ...
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