In this paper, a new finite-horizon H∞ control problem is considered for a class of time-
varying systems with stochastic nonlinearities, measurements degradation and chance …

This paper investigates the problem of designing data-driven stochastic Model Predictive
Control (MPC) for linear time-invariant systems under additive stochastic disturbance, whose …

For systems with soft state constraints, initial violation in such constraints is acceptable if no
feasible control strategy capable of maintaining such constraints exists or an excessively …

This paper presents two stochastic model predictive control methods for linear time-invariant
systems subject to unbounded additive uncertainties. The new methods are developed by …

In this paper, we present a Deep Reinforcement Learning (RL)-driven Adaptive Stochastic
Nonlinear Model Predictive Control (SNMPC) to optimize uncertainty handling, constraints …

This article considers linear discrete-time systems with additive disturbances and designs a
model predictive control (MPC) law incorporating a dynamic feedback gain to minimize a …

In this paper, we address the stochastic MPC (SMPC) problem for linear systems, subject to
chance state constraints and hard input constraints, under unknown noise distribution. First …

Chance constrained stochastic model predictive controllers (CC-SMPC) trade off full
constraint satisfaction for economical plant performance under uncertainty. Previous CC …

A data-driven stochastic MPC strategy is presented as an EMS for the Skagerak Energilab
microgrid. Uncertainties, introduced due to the intermittent nature of RES and load demands …

In this paper, a stochastic model predictive controller is designed for discrete time linear time
invariant systems, considering additive disturbance and stochastic constraints. As we know …