Mario Rotea - US grants

9358288 Rotea As in other areas of engineering, design of control systems involves tradeoffs among competing objectives. The basic problem is to find a controller that optimizes several competing performance and robustness requirements associated with one dynamical system. Good tracking of reference inputs vs low sensitivity to sensor noise, robust stability vs performance, are just a few examples of these tradeoffs. Clearly, it is important to develop tools to help the designer understand how the various competing objectives conflict with each other. One goal of this research is to obtain design procedures for the synthesis of controllers that meet several design specifications in the presence of uncertainty in the plant description. It is intuitively obvious that the performance of a given system may be greatly improved if the plant to be controlled and the controller are designed simultaneously rather than designing the plant first and then the controller. Unfortunately, in some cases of engineering relevance, an integrated plant/controller design is not feasible due to the large number of variables and design objectives. However, there are some challenging aerospace problems for which such an integration is not only feasible but also desirable. Another goal of this research is to develop a methodology for the design of systems (i.e. both plant and controller are to be designed) that satisfy multiple design specifications of practical significance. This research has three main components: (1) the development of new theoretical results for the analysis and synthesis of robust feedback systems with multiple design specifications, (2) the development of computational efficient algorithms to implement the theory, and (3) applications to validate the results. ***

0080832
Rotea

Several control engineering problems require the determination of parameters that optimize a system level cost function in real time. Real-time optimization is necessary to seek optimal control parameters in many application areas such as vibration and noise attenuation, flow separation, combustion control, and control of flying formations. In these problems the control architectures that improve system operation (e.g., minimize a noise or vibration figure, minimize flow separation in an airfoil, minimize the unsteady pressure fluctuation in a combustion chamber) are known in advance. On the other hand, the optimal control parameters are not known in advance and must be determined.

Off-line calculation of the optimal parameters is impractical when no reliable model is available to predict the variation of the cost function with time, the optimization parameters, or the system's operating conditions. It is generally possible, however, to make real-time measurements of the cost function through the addition of sensors and data processing. This additional hardware and software opens up the possibility of calculating optimal parameters by 'experimenting with the system' in order to determine the parameter setting that leads to a cost function improvement. The practical implementation of this idea requires an iterative algorithm that seeks the optimal parameters in real time.

The main objective of this project is the development of new iterative algorithms for the real-time optimization of a measurable cost function. Iterative 'extremum-seeking algorithms' that make use of function evaluations only, to estimate optimal parameters, will be considered. The challenge is to derive algorithms that (I) track fast variations in the optimal parameters, (II) are insensitive to the noise present in the measurements of the cost function, and (III) exhibit monotonic improvement of the cost function during the course of the optimization.

The focus shall be on 'gradient-based iterative algorithms' for which the necessary gradient information is not available and it must be estimated from the measurements of the cost function. Analysis of the algorithm performance, and sensitivity to modeling assumptions, will be carried out using methods and tools from the theory of nonlinear uncertain dynamical systems. Performance and sensitivity (or robustness) bounds will be obtained from the combination of traditional differential equations methods (averaging techniques) with recent tools from uncertain dynamical systems analysis. The bounds will later be used to synthesize algorithms that optimize criteria (I)-(III) above from available prior information.

If successful, the proposed research will advance the state-of-the-art of real-time gradient-based optimization algorithms. A novel framework for analysis that takes into account fast variations in optimal parameters and measurement noise will be developed. New systematic procedures to design extremum-seeking algorithms that work in uncertain noisy environments will be created. These design procedures would allow engineers to implement extremum-seeking algorithms with minimal development effort.
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Planning Grant for an I/UCRC for Wind Energy

0934321 University of Massachusetts Amherst (UMA); James Manwell
0934333 Old Dominion University Research Foundation (ODU); Larry Atkinson
0934325 James Madison University (JMU); Jonathan Miles

The Center for Wind Energy is aimed at enhancing national excellence in wind energy research and development of direct relevance to the industry; and, developing a cadre of diverse undergraduate and graduate students who will support and eventually lead in the design, manufacture, installation, operation, and maintenance of wind energy systems. The academic partners listed above are collaborating to establish the proposed center, with UMA as the lead institution.

The proposed Center is motivated by the possibility of integrating engineering with ocean and atmospheric sciences to support the development of systems with low cost of energy and high reliability. The thrust areas include: oceanography and geology as it relates to preparing wind turbine sites, turbine design, environmental effects, particularly impact on flying wildlife, land use, and system management and integration into the electrical grid. The proposed work has a very good focus on a well-defined area that is of long-term importance to energy generation in the US. The investigators have an excellent background, and will provide the needed expertise to expand the use of wind power for electrical generation.

The work by the proposed Center will make wind energy, particularly off-shore wind energy, more competitive with energy from non-renewable sources. The proposed Center would build on Amherst's wind research program, consolidating all aspects of wind power into a single unit. The Center plans to increase the diversity of participants in wind energy research and industry. Since UMA is the lead institution for the Northeast Alliance for Graduate Education and the Professoriate (NEAGEP), the NEAGEP infrastructure will be available to the proposed I/UCRC. ODU has strong research and academic ties with Hampton University and Norfolk State University, both designated as HBCUs. The long standing collaborations with these universities will be leveraged to promote the participation of minority students in wind energy projects.

This Partnerships for Innovation (PFI) project is a Type II (A:B) partnership, occurring within the University of Massachusetts Amherst with participation from the NSF PFI graduated grantee (0090521) in collaboration with participants from two other NSF partnership supported programs (both
I/UCRCs): Center for University of Massachusetts and Industry Research on Polymers (CUMIRP), which was founded in 1980 and has since graduated but is still active, and e-Design Center (0332508/0838747). The precision manufacturing sector, primarily Small and Mid-sized Enterprises (SMEs), is an important part of the economic base of Western Massachusetts with significant employment. The industry is currently challenged by cyclical markets, increased global competition, aging facilities/technologies and insufficient labor supply. The PFI program which was put in place in 2000 successfully established a regional industry network, Regional Technology Corporation (RTC), and this proposed program will enable significant enhancement and sustainability of technology transfer. This project will stimulate transformation of relevant new discoveries at UMass to SMEs that have little or no experience working with a research institution. Drawing upon the scientific and engineering research conducted at UMass, the university and the SMEs will collaborate on targeted and tailored research projects focused on translation and application. UMass facilities, state-of-the-art testing and characterization equipment, as well as its engineering design and management tools, will complement the project's translation and application process

The expected outcome of this program is a sustainable regional innovation infrastructure that supports effective transformation of the precision manufacturing SMEs to new markets through infusion of new technologies with a flexible and capable workforce. SMEs are a significant part of the U.S. economic engine and have contributed greatly to employment growth and economic development. The evaluation and assessment of this program should lead to important and transferable learning. The focus on enhancing technology transfer and translational work with SMEs, on partnering with regional assets, and on seeking additional financial support should ensure that the impacts of the program are meaningful, documented, disseminated and sustained.

Partners at the inception of the project are Academic Institutions: University of Massachusetts Amherst (lead institution), including participation of the Office of the Vice Chancellor of Research and Engagement, Office of Research Liaison and Development, Office of Commercial Ventures and Intellectual Property, Polymer Science and Engineering Department, Department of Mechanical and Industrial Engineering, Center for UMass-Industry Research on Polymers, Center for e-Design, and Department of Landscape Architecture and Region Planning; and Holyoke Community College; Private Sector Organizations: Ben Franklin Design and Manufacturing Company, Inc.; State and Regional Organizations: Regional Employment Board of Hampden County, Inc., MA; and Regional Technology Corporation,(RTC), MA. Other participating organizations and personnel include Academic: Springfield Technical Community College; and State and Regional Organizations: Economic Development Council for Western Massachusetts, and Western Mass Chapter-National Tooling and Machining Association (WMNTMA).

Planning Grant for an I/UCRC for Wind Energy, Science, Technology, and Research (WIndSTAR)

1238307 University of Massachusetts Lowell; Christopher Niezrecki
1238314 Texas A&M; John Niedzwecki
1238302 University of Texas at Dallas; Mario Rotea
1238185 Iowa State University; Matthew Frank

The proposed Center for Wind Energy, Science, Technology, and Research (WIndSTAR) will aim to enhance national excellence in wind energy research and development that has direct relevance to industry, and to develop a cadre of diverse undergraduate and graduate students with world-class training who will support and eventually lead in the analysis, design, manufacture, installation, operation, and maintenance of wind energy systems. The research efforts will be anchored by the University of Massachusetts Lowell as the lead institution, partnering with Texas A&M, the University of Texas at Dallas, and Iowa State University.

Wind energy systems for electricity production are complex engineered systems that operate on land or over water to produce usable power. These systems consist of an array of wind turbines placed on appropriate support structures and interconnected to deliver electric power to the utility grid. The potential for land-based and offshore wind energy is tremendous, but to achieve this potential will require a coherent and industry-relevant research and development program that involves industry, academe and government. The proposed I/UCRC will address industrially relevant research to advance and support the development of wind energy systems with low-cost energy and high reliability. The partners intend to engage in a cooperative program of research and education in the following areas: A) Composites in Wind Energy, B) Foundations and Towers, C) Manufacturing and Design, D) Structural Health Monitoring, Non-Destructive Inspection, and Testing,E) Control Systems and Storage, and F) Wind System Planning, Siting, Operations, and Maintenance.

The proposed center will provide a forum in which wind turbine manufacturers, manufacturers of key components, suppliers of ancillary equipment, service companies, and wind project developers can work together to solve problems that are of mutual interest. In addition, the center will work to develop and integrate educational activities that enhance recruitment and retention of diverse student populations. The Center intends to collaborate with KidWind, which provides teaching materials for K-12 teachers, and manages regional and national challenges for team turbine design competitions. The center will provide a conduit for the transfer of ideas among KidWind, industry, and academe and will engage industrial partners to develop regional training programs between educators and industry to create a sustainable pipeline of future STEM workers with strong interest by women and underrepresented groups.

The I/UCRC for Wind Energy Science, Technology and Research plans to integrate engineering with fundamental research to support the development of wind energy systems for production of low-cost energy with high reliability. The center and its partners intend to engage in a cooperative program of research and education in the following key areas: (a) Composites, (b) Foundations, Towers, and Infrastructure (c) Manufacturing and Design, (d) Structural Health Monitoring, Non-Destructive Inspection, and Testing, (e) Control Systems and Energy Storage, and (f) Wind System Planning, Siting, and Operations.

The proposed I/UCRC will have broad impacts with respect to the environment, the economy, and education. With large minority populations near Lowell and Dallas, the Center is well situated to involve underrepresented groups within the wind energy field. The Center intends to leverage best practices from several successful programs to develop and integrate educational activities that enhance recruitment and retention of diverse student populations and encourage under-represented minority and female students to pursue STEM careers. In addition to workforce development and R&D, WindSTAR will provide a forum in which multiple facets of the wind industry (e.g., wind turbine manufacturers, manufacturers of key components, materials suppliers, suppliers of ancillary equipment, service companies, and wind project developers) can work together to solve precompetitive problems that hinder the advancement of wind energy.

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.