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 …

Abstract Machine learning (ML) has recently gained in popularity, spurred by well-publicized
advances like deep learning and widespread commercial interest in big data analytics …

This paper provides an introduction to Reinforcement Learning (RL) technology,
summarizes recent developments in this area, and discusses their potential implications for …

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 …

This paper presents an adaptive learning method to achieve the output-feedback robust
tracking control of systems with uncertain dynamics, which uses the techniques developed …

As the demand for energy continues to grow, optimization has risen to the forefront of power
engineering research and development. Continuing in the bestselling tradition of the first …

Hydrogen can be produced in an environmentally friendly manner through biological
processes using a variety of organic waste and biomass as feedstock. However, the …

The modernization of the US electric power infrastructure, especially in lieu of its aging,
overstressed networks; shifts in social, energy and environmental policies, and also new …

This paper outlines a novel approach to the design of optimal space trajectories under
significant uncertainty. Finite-horizon covariance control, ie, the steering of a system from an …

This article investigates model predictive control (MPC) of linear systems subject to arbitrary
(possibly unbounded) stochastic disturbances. An MPC approach is presented to account …