Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …

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 …

In this article, we present a nonlinear robust model predictive control (MPC) framework for
general (state and input dependent) disturbances. This approach uses an online …

This paper provides an overview of the recent research efforts on the integration of machine
learning and model predictive control under uncertainty. The paper is organized as a …

Constraint tightening to non-conservatively guarantee recursive feasibility and stability in
Stochastic Model Predictive Control is addressed. Stability and feasibility requirements are …

An output feedback stochastic model predictive control is proposed in this article for a class
of stochastic linear discrete-time systems, in which the uncertainties from external …

In the era of Industrial 4.0, the next-generation control system regards the cyber-physical
system (CPS) as the core ingredient thanks to the comprehensive integration of physical …

Solving nonlinear model predictive control problems in real time is still an important
challenge despite of recent advances in computing hardware, optimization algorithms and …

This literature review extends and contributes to research on the development of data-driven
optimal control. Previous reviews have documented the development of model-based and …

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