Model predictive control (MPC) has demonstrated exceptional success for the high-
performance control of complex systems. The conceptual simplicity of MPC as well as its …

In the past ten years many Stochastic Model Predictive Control (SMPC) algorithms have
been developed for systems subject to stochastic disturbances and model uncertainties …

Controlling soft robots with precision is a challenge due to the difficulty of constructing
models that are amenable to model-based control design techniques. Koopman operator …

Controlling soft robots with precision is a challenge due in large part to the difficulty of
constructing models that are amenable to model-based control design techniques …

Nonlinear dynamical systems can be made easier to control by lifting them into the space of
observable functions, where their evolution is described by the linear Koopman operator …

In autonomous systems, the ability to make forecasts and cope with uncertain predictions is
synonymous with intelligence. Model predictive control (MPC) is an established control …

Data-driven control based on the fundamental lemma by Willems et al. is frequently
considered for deterministic linear time invariant (LTI) systems subject to measurement …

Data‐driven predictive control based on the fundamental lemma by Willems et al. is
frequently considered for deterministic LTI systems subject to measurement noise. However …

Dynamic simulation of stochastic systems requires uncertainty propagation. Traditional
sample-based uncertainty propagation methods are often computationally intractable for …

Abstract Systems of linear ordinary differential equations are examined, subject to real-
random parametric uncertainty. Specifically, the paper considers stability and norm …