Stochastic model predictive control: An overview and perspectives for future research
A Mesbah - IEEE Control Systems Magazine, 2016 - ieeexplore.ieee.org
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 …
performance control of complex systems. The conceptual simplicity of MPC as well as its …
Machine learning: Overview of the recent progresses and implications for the process systems engineering field
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 …
advances like deep learning and widespread commercial interest in big data analytics …
Reinforcement learning–overview of recent progress and implications for process control
This paper provides an introduction to Reinforcement Learning (RL) technology,
summarizes recent developments in this area, and discusses their potential implications for …
summarizes recent developments in this area, and discusses their potential implications for …
Fusion of machine learning and MPC under uncertainty: What advances are on the horizon?
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 …
learning and model predictive control under uncertainty. The paper is organized as a …
Output-feedback robust tracking control of uncertain systems via adaptive learning
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 …
tracking control of systems with uncertain dynamics, which uses the techniques developed …
[图书][B] Electric power system applications of optimization
JA Momoh - 2017 - taylorfrancis.com
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 …
engineering research and development. Continuing in the bestselling tradition of the first …
Machine learning in fermentative biohydrogen production: advantages, challenges, and applications
Hydrogen can be produced in an environmentally friendly manner through biological
processes using a variety of organic waste and biomass as feedstock. However, the …
processes using a variety of organic waste and biomass as feedstock. However, the …
Smart grid design for efficient and flexible power networks operation and control
JA Momoh - 2009 IEEE/PES Power Systems Conference and …, 2009 - ieeexplore.ieee.org
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 …
overstressed networks; shifts in social, energy and environmental policies, and also new …
Reinforcement learning for batch process control: Review and perspectives
Batch or semi-batch processing is becoming more prevalent in industrial chemical
manufacturing but it has not benefited from advanced control technologies to a same degree …
manufacturing but it has not benefited from advanced control technologies to a same degree …
Adaptive learning in tracking control based on the dual critic network design
In this paper, we present a new adaptive dynamic programming approach by integrating a
reference network that provides an internal goal representation to help the systems learning …
reference network that provides an internal goal representation to help the systems learning …