This paper studies a probabilistic interpretation of input-to-state stability (ISS) bounds for estimation-error dynamics in continuous-time systems. We show that, if the aggregated disturbance satisfies a probabilistic envelope in an essential-supremum sense, then deterministic ISS bounds immediately induce high-probability error tubes. To make this interpretation constructive, we also provide explicit sufficient conditions based on quadratic Lyapunov inequalities and specialize them to positive and cooperative systems. The approach is illustrated on a positive compartment model with aggregated measurements, where ISS tubes are compared with nominal uncertainty bands produced by a Kalman--Bucy filter and by Gaussian and robust moving-horizon estimators. The examples show that ISS tubes provide a conservative but computationally light uncertainty baseline, while robust MHE is less sensitive to outlier contamination than Gaussian-based
State estimates used in sampled monitoring and automation need bounds that remain valid between measurements. We develop a finite-horizon input-to-state-stability tube and observer co-design framework for continuous-time observers driven by sampled and held outputs. The sampled-d…
For continuous-time linear quadratic regulation with unknown system matrices, data-driven off-policy policy iteration typically estimates the value matrix and the improved feedback gain through a joint critic--actor regression. We show that the critic is not needed in the policy-…
We study a decentralized online estimation problem with additive communication noises over the fixed digraph. Each node has a linear measurement of an unknown parameter with random measurement matrices and runs a continuous-time online estimation algorithm. We transform the conve…
We study timestep allocation for score-based diffusion sampling, where a learned reverse-time dynamics is discretized on a finite grid. Uniform and hand-crafted schedules are standard choices, but they rely on fixed prescriptions and can therefore be suboptimal. To address this l…
Reliable, low-latency communication is critical for real-time monitoring and control in modern Smart Grids (SGs). The emergence of 5G networks, with enhanced reliability, significantly lower latency, and native support for massive machine-type communication, offers strong potenti…
This article presents a novel, numerically viable algorithm for solving sparse robust optimal control problems in continuous time. We consider a constrained linear noisy system governed by an ordinary differential equation (ODE), with an $L^1$-type objective function in line with…