EAGER: Real-Time: Decision and Control of Complex Engineered Systems Enabled by Machine Learning and High-performance Computing

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In recent years, there have been significant advances in machine learning - statistical techniques that enable computers to "learn" using available data. Machine learning methods have demonstrated great success in image recognition, language translation, speech processing, and other consumer applications. This has led to great interest globally in academia, industry, and government. The drawback in purely machine learning methods is that it does not use the knowledge of physical properties of specific system which could significantly improve the performance of these methods. This EArly-concept Grant for Exploratory Research (EAGER) project will lead to fundamental results and methods that combine the advantages of machine learning techniques and knowledge of physical attributes of the system to enable decision making and control of complex engineered systems. The research will be conducted in the context of control of large wind energy plants. Maximizing power production despite variable and uncertain operating conditions in large wind plants is an unsolved problem that is ripe for transformative approaches and innovation. The research from this project is likely to transition to industry by leveraging connections with the NSF I-UCRC for Wind Energy Science, Research and Technology (WindSTAR) as wind plant owners and operators constantly seek new ways to improve annual energy production and reduce the cost of electricity from wind.The main idea of this EAGER project is to leverage advances in deep learning and high performance computing simulations for the control of complex engineered systems. Our hypothesis is that techniques from (semi-supervised) machine learning can be tailored to extract information from high performance simulation data to deal with the joint problem of identifying control system architectures and control algorithms for real-time decision making in complex engineered systems. The research goals of this project have great potential to contribute to the convergence of high performance computing simulations and data, machine learning, and controls to advance the state-of-art tools for controlling complex engineered systems. The testbed for the project is a wind plant. As turbines become larger, and are placed closer to one another, the aerodynamic coupling amongst turbines will increase resulting in a truly large-scale complex engineered system that must perform despite environmental uncertainty and variability of turbine components. Specific goals of this project include: Advanced learning algorithms for extracting control system architecture and training algorithms from large eddy simulation data of the wind farms; Real-time decision algorithms to select architecture and algorithms from site-specific libraries discovered in the first goal; and Real-time algorithms for tuning key parameters of the control solutions for additional improvements in the overall energy production.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.