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Frameworks and results in distributionally robust optimization
The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …
have developed significantly over the last decade. The statistical learning community has …
A review on monitoring and advanced control strategies for precision irrigation
The demand for freshwater is on the increase due to the rapid growth in the world's
population while the effect of global warming and climate change cause severe threat to …
population while the effect of global warming and climate change cause severe threat to …
[HTML][HTML] Model predictive control of water resources systems: A review and research agenda
Abstract Model Predictive Control (MPC) has recently gained increasing interest in the
adaptive management of water resources systems due to its capability of incorporating …
adaptive management of water resources systems due to its capability of incorporating …
[HTML][HTML] Data analytics and machine learning for smart process manufacturing: Recent advances and perspectives in the big data era
Safe, efficient, and sustainable operations and control are primary objectives in industrial
manufacturing processes. State-of-the-art technologies heavily rely on human intervention …
manufacturing processes. State-of-the-art technologies heavily rely on human intervention …
An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment
Relieving the stress from energy demand is critical for encouraging the application of built
environment in agriculture, which is the most energy-intensive food-production sector. In this …
environment in agriculture, which is the most energy-intensive food-production sector. In this …
Soil moisture forecast for smart irrigation: The primetime for machine learning
The rise of the Internet of Things allowed higher spatial–temporal resolution soil moisture
data captured through in situ sensing. Such abundance of data enables machine learning …
data captured through in situ sensing. Such abundance of data enables machine learning …
Intelligent control and energy optimization in controlled environment agriculture via nonlinear model predictive control of semi-closed greenhouse
Greenhouse climate is a highly complex system that contains nonlinearity and
dependencies between each system state. This paper proposes a novel nonlinear model …
dependencies between each system state. This paper proposes a novel nonlinear model …
[HTML][HTML] Multi-zone building control with thermal comfort constraints under disjunctive uncertainty using data-driven robust model predictive control
This paper proposes a novel data-driven robust model predictive control (MPC) framework
for a multi-zone building considering thermal comfort and uncertain weather forecast errors …
for a multi-zone building considering thermal comfort and uncertain weather forecast errors …
Sustainable power systems operations under renewable energy induced disjunctive uncertainties via machine learning-based robust optimization
For sustainable and reliable power systems operations integrating variable renewable
energy, it is essential to incorporate the uncertain intermittent power outputs. A novel robust …
energy, it is essential to incorporate the uncertain intermittent power outputs. A novel robust …
Semiclosed greenhouse climate control under uncertainty via machine learning and data-driven robust model predictive control
This work proposes a novel data-driven robust model predictive control (DDRMPC)
framework for automatic control of greenhouse in-door climate. The framework integrates …
framework for automatic control of greenhouse in-door climate. The framework integrates …