Education

Honors and Awards

• UNC-Charlotte College of Engineering Excellence in Teaching Award, 2017
• UNC-Charlotte Maxheim Faculty Fellowship, 2016
• NSF CAREER Award, 2015

Grants

Persistent Mission Planning and Control for Renewably Powered Robotic Systems

National Science Foundation (NSF)(6/01/20 - 5/31/23)

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Persistent Mission Planning and Control for Renewably Powered Robotic Systems

Sponsor: National Science Foundation (NSF)

Start Date: 6/01/20

End Date: 5/31/23

Abstract

The objective of the proposed research is to pioneer and validate persistent mission planning and control frameworks for robotic systems whose primary or sole propulsion source is a renewable, but intermittent, resource. Examples of such systems include sailing drones, tumbleweed rovers, and solar-powered aircraft. Unlike traditional mobile robotic systems (e.g., quad-rotors, wheeled mobile robots), which possess rather predictable mobility but limited range, renewably powered robotic systems completely shift this paradigm by providing unlimited range, with the complication that mobility is dictated by an intermittent, stochastic resource. This paradigm shift demands a fundamentally different approach to mission planning and control. The proposed research will focus on the creation and validation of hierarchical mission planning/control frameworks whereby an upper-level mission planner optimally selects exploration directions that maximize a statistical measure of information under a stochastic model of mobility, and a time-optimal lower-level control system is used to maximize expected mobility in real time. Both levels of optimization will make reference to statistical estimates of achievable mobility, as opposed to assured constraints on mobility (as would be the case with traditional mobile robotic systems) in performing their respective optimizations. The proposed mission planning and control frameworks will be applied to a team of robotic sailboats, which are presently being commissioned at PI Vermillion’s Control and Optimization for Renewables and Energy Efficiency (CORE) Lab at NC State and will ultimately be sailed in inland waters. The proposed education and outreach plan includes participation of students in the activities of Autonomous Marine Systems, an early-stage company pioneering larger-scale sailing drones, and the coordination of physics-based sailing instruction activities in conjunction with Carolina Sailing Club.

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Collaborative Research: Workshop on Integrated Design of Active Dynamic Systems

National Science Foundation (NSF)(9/01/19 - 8/31/21)

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Collaborative Research: Workshop on Integrated Design of Active Dynamic Systems

Sponsor: National Science Foundation (NSF)

Start Date: 9/01/19

End Date: 8/31/21

Abstract

The objective of this proposed workshop is to bring together scientific expertise from the dynamics and controls and the engineering design communities to address fundamental research questions at the interface of physical and control system design. This effort has the potential to lead to the creation of a new research community that can move forward with 1) theoretical frameworks for active system design, 2) creation and analysis of integrated active system design methods with new levels of design and modeling fidelity, and 3) development of tools for engineering practice, such as design automation methods and validated design guidelines, with potential for significant societal impact.

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CAREER: Efficient Experimental Optimization for High-Performance Airborne Wind Energy Systems

National Science Foundation (NSF)(8/16/18 - 8/31/21)

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CAREER: Efficient Experimental Optimization for High-Performance Airborne Wind Energy Systems

Sponsor: National Science Foundation (NSF)

Start Date: 8/16/18

End Date: 8/31/21

Abstract

This Faculty Early Career Development (CAREER) grant will pioneer a first-in-world lab-scale system for optimizing the flight dynamics and control of airborne wind energy systems. These systems replace towers with tethers and a lifting body, enabling operation in strong, high-altitude winds. Successful deployment is projected to result in levelized costs of energy below $0.25 per kW-h, providing cost-competitive energy solutions to remote communities, islands, military bases, and deep-water offshore locations. However, the synthesis of control systems that stabilize airborne wind energy systems in harsh atmospheric conditions remains a burden to their widespread acceptance, which is further exacerbated by high prototype development costs. This research will reduce control system prototyping costs by multiple orders of magnitude, using 1/100-scale models that are 3D printed, tethered, and “flown” in the University of North Carolina at Charlotte water channel. The water channel provides an ideal mechanism for optimizing the control system design while replicating key dynamic properties of the full-scale system. Throughout the project, select students will interact with Altaeros Energies, a leading early-stage airborne wind energy company based in Boston. Outreach activities include the development of kite design modules for a high school engineering summer camp and co-design of an energy-rich science curriculum for an early college high school for economically disadvantaged students.

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Collaborative Research: An Economic Iterative Learning Control Framework with Application to Airborne Wind Energy Harvesting

National Science Foundation (NSF)(8/16/18 - 5/31/22)

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Collaborative Research: An Economic Iterative Learning Control Framework with Application to Airborne Wind Energy Harvesting

Sponsor: National Science Foundation (NSF)

Start Date: 8/16/18

End Date: 5/31/22

Abstract

The objective of the proposed research is to derive new control theoretic knowledge for a unique economic iterative learning control (ILC) framework that focuses on maximizing/minimizing a profitability index rather than mere tracking performance. The proposed framework blends two key mechanisms that will transform the current state-of-the-art in point-to-point ILC. Specifically, the approach will include: 1. An outer loop waypoint update law that blends exploration (trying new sets of waypoints to learn how new combinations of waypoints impact performance) with exploitation (adjusting waypoints in the direction of the perceived optimum under known information); 2. An inner loop flexible-time point-to-point ILC update law that uses waypoint position and performance information related to the profitability index from the previous iteration to compute an updated input signal, which is discretized in a spatial, rather than temporal domain. The proposed approach will be validated, in simulation and through a small-scale experimental framework, on an airborne wind energy (AWE) application. In this application, tethers and a lifting body (kite, wing, or aerostat) are used in place of a tower to reduce material requirements and reach high altitudes. In such systems, it has been shown that dramatically increased power production is achievable through repetitive crosswind (figure 8 or circular) flight. However, existing system models are not sufficiently accurate to determine the optimal trajectory offline; iteration-to-iteration learning can serve as a critical enabling methodology for realizing the full power production potential of these systems.

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Collaborative Research: Multi-Scale, Multi-Rate Spatiotemporal Optimal Control with Application to Airborne Wind Energy Systems

National Science Foundation (NSF)(8/16/18 - 8/31/20)

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Collaborative Research: Multi-Scale, Multi-Rate Spatiotemporal Optimal Control with Application to Airborne Wind Energy Systems

Sponsor: National Science Foundation (NSF)

Start Date: 8/16/18

End Date: 8/31/20

Abstract

The objective of the proposed research is to create and validate a unique multi-scale, multi-rate hierarchical control framework for spatiotemporally varying systems. The research will be valuable for numerous emergent applications where performance is impacted by an environmental field that varies as a function of both time and a controllable spatial variable. The proposed mathematical framework will alleviate computational hurdles that render centralized, global optimal control computationally intractable. The fundamental methods will be applied to an airborne wind energy (AWE) system model, which can adjust its altitude to maximize power output. Real wind vs. altitude vs. time data will be used to validate the mathematical tools.

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Maximizing Vehicle Fuel Economy Through the Real-Time, Collaborative, and Predictive Co-Optimization of Routing, Speed, and Powertrain Control

US Dept. of Energy (DOE) - Advanced Research Projects Agency - Energy (ARPA-E)(7/01/18 - 3/27/20)

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Maximizing Vehicle Fuel Economy Through the Real-Time, Collaborative, and Predictive Co-Optimization of Routing, Speed, and Powertrain Control

Sponsor: US Dept. of Energy (DOE) - Advanced Research Projects Agency - Energy (ARPA-E)

Start Date: 7/01/18

End Date: 3/27/20

Abstract

The proposed research will result in optimal control strategies that realize fuel consumption improvements of over 20 percent for heavy-duty trucks, using hierarchical control strategies that leverage intra- and inter-vehicle connectivity. Specifically, the optimal control approaches developed in the research will blend velocity and route optimization schemes at the vehicle level with powertrain optimization schemes that ensure that drivability is not sacrificed when minimizing fuel consumption. Furthermore, the research will leverage vehicle-to-infrastructure communications capabilities to optimize approach to and departure from signalized intersections in order to save tremendous amounts of fuel on heavy-duty vehicles that otherwise must sacrifice significant kinetic energy in stop-and-go traffic. All algorithms will undergo progressive validation, beginning with software-in-the-loop validation, progressing to hardware-in-the-loop validation, and culminating with in-vehicle validation.

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Device Design and Robust Periodic Motion Control of an Ocean Kite System for Hydrokinetic Energy Harvesting

US Dept. of Energy (DOE) - Energy Efficiency & Renewable Energy (EERE)(5/01/19 - 4/30/22)

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Device Design and Robust Periodic Motion Control of an Ocean Kite System for Hydrokinetic Energy Harvesting

Sponsor: US Dept. of Energy (DOE) - Energy Efficiency & Renewable Energy (EERE)

Start Date: 5/01/19

End Date: 4/30/22

Abstract

This project will focus on the model-based design, flight characterization, robust periodic control, 1/10-scale prototyping, and testing of a rigid kite-based ocean current and tidal energy harvesting system. The system is intended for areas of moderate flow in relatively shallow waters, one example being the shallow waters adjacent to the Gulf Stream. The proposed system will consist of a high lift/drag rigid wing that executes periodic cycles. Each cycle will consist of a high-tension cross-current spool-out phase, followed by a low-tension spool-in phase. The use of multiple control tethers and/or on-board control surfaces will make it possible to achieve and control this desired periodic motion in a manner that is robust to fluctuations and uncertainties (e.g., ocean current speed/direction, etc.). It has been demonstrated that properly controlled periodic motions can lead more than an order of magnitude increase in net energy production over equivalently-sized stationary devices, or equivalently, the same amount of power as a stationary system using an order of magnitude less material. The proposed research will focus on two candidate kite-based system configurations: (i) A system where the electric motors/generators and power electronics are housed on a floating platform out of the water and (ii) a system where they are housed at the seabed, in shallower waters.

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