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Learning-based model predictive control: Toward safe learning in control
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 …
sensing and computational capabilities in modern control systems, have led to a growing …
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 …
Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming
This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …
modern data lens, highlights key research challenges and promise of data-driven …
A survey of adjustable robust optimization
Static robust optimization (RO) is a methodology to solve mathematical optimization
problems with uncertain data. The objective of static RO is to find solutions that are immune …
problems with uncertain data. The objective of static RO is to find solutions that are immune …
Model predictive control
One of the motivations behind this book was to collect together the many results of the
Oxford University predictive control group. For this reason we have, rather unashamedly …
Oxford University predictive control group. For this reason we have, rather unashamedly …
[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 …
A practical guide to robust optimization
Robust optimization is a young and active research field that has been mainly developed in
the last 15 years. Robust optimization is very useful for practice, since it is tailored to the …
the last 15 years. Robust optimization is very useful for practice, since it is tailored to the …
From predictive to prescriptive analytics
We combine ideas from machine learning (ML) and operations research and management
science (OR/MS) in develo** a framework, along with specific methods, for using data to …
science (OR/MS) in develo** a framework, along with specific methods, for using data to …
Uncertainty-aware deployment of mobile energy storage systems for distribution grid resilience
With the spatial flexibility exchange across the network, mobile energy storage systems
(MESSs) offer promising opportunities to elevate power distribution system resilience …
(MESSs) offer promising opportunities to elevate power distribution system resilience …
Preventing undesirable behavior of intelligent machines
Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple
data analysis and pattern recognition tools to complex systems that achieve superhuman …
data analysis and pattern recognition tools to complex systems that achieve superhuman …