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
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…
This paper investigates the control of discrete-time linear time-invariant (LTI) systems subject to incomplete and corrupted measurements. Specifically, we focus on designing a Linear Quadratic Gaussian (LQG) controller without relying on explicit state estimation. By leveraging…
This paper studies learning-based MPC for constrained stabilization of discrete-time linear systems with unknown system parameters and additive bounded disturbances. We develop a tractable homothetic-tube MPC scheme in which a high-probability parameter confidence set is generate…
A common way to design observers is to add a correction term to a copy of the system; however, designing the correction term for nonlinear systems remains a significant long-standing challenge. Contraction theory offers a unified approach to designing this correction term by solv…
This paper studies distributed adaptive state estimation for discrete-time nonlinear systems with unknown source dynamics over directed communication networks. Each sensing agent estimates the source state using only local measurements and information exchanged with neighboring a…
We investigate a decentralized reinforcement learning problem involving multiple agents that interact with the same Markov Decision Process (MDP). The agents can exchange information over a network to collectively learn the optimal state-action value function. For this setting, w…