About

Dr. Matthew Bryant is a professor in the Department of Mechanical and Aerospace Engineering at NC State University. His research interests encompass a multidisciplinary approach that combines smart materials, dynamical systems, and fluid-structure interaction phenomena. His work aims to create novel systems for energy harvesting, sensing, and actuation. In addition to his research and teaching activities, Dr. Bryant enjoys outdoor activities such as fishing, hiking, kayaking, and nature photography.

Research topics

• Computer Science
• Engineering
• Artificial Intelligence
• Aerospace engineering
• Mechanical engineering
• Mechanics
• Physics
• Electrical engineering
• Marine engineering
• Materials science
• Structural engineering
• Automotive engineering
• Simulation
• Composite material
• Aeronautics
• Mathematics
• Control engineering
• Telecommunications

Selected publications

• Optimal Cyclic Control of a Structurally Constrained Morphing Energy-Harvesting Kite Using an Experimentally Validated Simulation Model

  IEEE Transactions on Control Systems Technology · 2025-01-09 · 2 citations

  article

  This work presents an experimentally validated dynamic model, control trajectory optimization methodology, and representative simulation results for a morphing underwater kite. Morphing, defined as real-time modification of the kite’s geometry to either curtail structural loading or enhance power generation, is motivated by the fact that the optimal design of an energy-harvesting kite is highly sensitive to flow speed and tether length, particularly in the presence of structural limitations that render load curtailment necessary at high flow speeds and short tether lengths. To achieve morphing behavior, an inboard Fowler flap (capable of modifying the chord and camber of an inboard wing section) was employed in tandem with a symmetric aileron bias, enabling simultaneous control over both the wing’s overall lift coefficient and center of lift without requiring the mechanical complexity associated with span morphing. The effects of these morphing parameters were integrated into an existing dynamic simulation framework, and experiments were conducted using a customized scaled tow testing setup to refine and experimentally validate the simulation model. Following the refinement of this model, a morphing trajectory optimizer was designed to optimize the morphing input trajectories over a spooling cycle using flow data from the previous cycle. Finally, using the refined simulation model and multicycle controller, simulations of large-scale kites operating in a realistic flow environment were conducted. In these simulations, a kite capable of morphing was shown to generate between 8.1% and 25.3% more energy than non-morphing kite designs.

  PublisherDOI

• Fused Portfolio Optimization for Harnessing Marine Renewable Energy Resources

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Model-Based Control Allocation During State Transitions of a Variable Recruitment Fluidic Artificial Muscle Bundle

  Actuators · 2025-05-08

  articleOpen accessSenior author

  A model-based control scheme for state transitions of a variable recruitment fluidic artificial muscle (FAM) bundle is developed and experimentally validated. FAMs can be bundled together in parallel to exhibit variable recruitment functionality, which is an activation strategy inspired by how motor units (MUs) in skeletal muscle are recruited. By adapting variable recruitment, an FAM bundle is able to operate efficiently over its entire force-contraction space while increasing control authority and bandwidth at low recruitment states. A variable recruitment bundle poses a hybrid control problem as it operates by controlling pressure as a continuous variable while simultaneously shifting between discrete recruitment states. During such state transitions, the bundle may experience a lag in strain if the shift timing is not properly anticipated. In this study, a model that captures the interaction effects between FAMs and a hydraulic system model is used to inform the controller of when a state transition should be made. The proposed control scheme is compared to a baseline control scheme that uses a percentage of the source pressure as the threshold for when a shift is made. The controller performance is evaluated by tracking a sinusoidal strain trajectory, and the average and maximum strain errors are compared between the baseline and proposed controller. The applied FAM pressures are presented to show that the model-based compensation is able to determine when a transition needs to be made. As a result, the tracking performance of the proposed control scheme is shown to significantly decrease the integrated absolute and maximum errors.

  PublisherOA PDFDOI

• Fused portfolio optimization for harnessing marine renewable energy resources

  Energy · 2025-12-12

  articleOpen access

  Offshore wind and marine hydrokinetic energy are underutilized energy resources. Efficiently exploiting these energy resources requires the identification of optimal deployment locations and optimal designs for offshore energy harvesting devices. These devices have the potential to be deployed in tandem such that the suite of devices consistently saturates a given power transmission system. To better understand the economic viability of harvesting marine renewable energy, a portfolio optimization is presented here. Portfolio optimization frameworks help to identify optimal deployment maps for energy-harvesting devices in a given domain and unify solutions of resource, technical performance, transmission, and cost model sub-problems into a unique and comprehensive tool. These frameworks select the energy-harvesting device designs in advance. This work proposes a portfolio optimization framework combined with optimal device design, sizing, and selection to enable a more realistic energy depiction that is beneficial to stakeholders. By maximizing power sent back to shore subject to a constraint on the levelized cost of energy, the algorithm creates an optimal mapping of devices that produces the maximum transmittable power and stabilizes portfolio variability in a cost-effective manner. Any reliably modeled offshore energy-harvesting device can be used within this framework. In this work, wind turbines and marine hydrokinetic kites are selected as a case study considering they are leading technologies for harvesting their respective energies. Results from this case study demonstrate optimal portfolios of devices for a location off the coast of North Carolina and show the utility of fusing device design optimization with the portfolio optimization. • Reliable, transparent marine hydrokinetic energy-harvesting kite design model developed. • Novel fusion of energy-harvesting device optimization and portfolio optimization. • Fusion can result in lower costs per unit energy across the entire deployment.

  PublisherDOI

• Extremum Seeking-Based Power Maximization in a Wave-Driven Glider

  2025-08-25

  article

  Wave energy is a promising renewable resource, yet traditional wave energy conversion (WEC) systems suffer from limitations due to their stationary deployment. In particular, stationary WECs require several months or even years for permitting and installation, and they cannot be relocated to suit evolving demands after their installation. While these limitations are not necessarily an issue for longterm deployments, they are problematic for applications such as disaster recovery or temporary power supplementation in island communities, which require rapid deployability. This work examines a mobile wave glider system that combines the principles of a rapidly deployable wave glider with an auxiliary power take-off (PTO) system that utilizes a fraction of the available wave power to charge an on-board battery through an active damper. The contribution of this work lies in a real-time power optimization scheme that combines a sea-state-driven, model-based lookup table (based on a customized model developed by the authors) with extremum seeking control (ESC) to learn a correction to the lookup table. This approach is based on the observation that the optimal corrections will exhibit limited, slow variation relative to the variations in the sea state itself. Simulations, driven by wave data from the island nation of Palau and a scaled model of the system, demonstrate the effectiveness of the proposed control strategy in achieving near-optimal energy harvesting.

  PublisherDOI

• Analysis and Experimental Validation of a Low-Complexity Enhanced Orientation-Based Controller for Tethered Energy-Harvesting Systems

  IEEE Transactions on Control Systems Technology · 2025-04-28 · 1 citations

  articleOpen access

  In this work, a methodology for controlling the flight of an underwater energy-harvesting kite, termed enhanced orientation-based control, is presented. This control technique is shown to perform comparably to more complex, hierarchical path-following control approaches that rely upon expensive and unreliable localization sensors while performing significantly better than simple orientation-based controllers that possess a comparable degree of complexity. The periodic closed-loop stability of a kite utilizing the proposed controller is validated in a low-order simulation framework. From there, the performance of the proposed controller is benchmarked against established control techniques via a medium-fidelity simulation environment. Finally, the efficacy of the proposed controller design is demonstrated experimentally based on two testing results on a scaled prototype kite.

  PublisherOA PDFDOI

• Automatic Limit Cycle Oscillation Annihilation in Aeroelastic Wings Using Prescribed Impinging Vortices

  AIAA Journal · 2025-10-28

  articleSenior author
  PublisherDOI

• Fused Portfolio Optimization for Harnessing Marine Renewable Energy Resources

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Marine Hydrokinetic Farm Optimization for Coaxial Dual-Rotor Turbines

  IEEE Journal of Oceanic Engineering · 2024-07-22 · 1 citations

  article

  This article focuses on the optimization of marine hydrokinetic farms of coaxial dual-rotor turbines with wake interaction. To perform the optimization, we introduce a new analytical wake model for this turbine configuration and validate it herein. The proposed model predicts the wake velocity deficit in the near- and far-wake of the turbine in terms of the diameters and axial induction factors of the upstream and downstream rotors and the location of the near-wake boundary. It is derived by utilizing mass- and momentum balancing in the near- and far-wake control volumes, supplemented by the application of Bernoulli's principle along pertinent streamlines. The analytical prediction is compared with computational simulation results for different flow conditions to find good agreement between them. The optimization problem is solved by the implementation of a genetic algorithm, which is developed based on the wake model. The algorithm maximizes farm efficiency by minimizing the wake interactions among the turbines. The influence of different parameters of the algorithm on its overall performance and efficiency is investigated to discover that a perfect integration among the parameters is essential for a successful search. Eventually, three different cases are studied with different farm sizes, numbers of cells in farm layouts, and aspect ratios of the farm at each of the flow conditions to illustrate the functionality and robustness of the algorithm that is based on the proposed wake model. The optimization results will be useful for the assessment of the hydrokinetic power potential of such turbine configurations in an ocean or riverine current.

  PublisherDOI

• Design, Prototyping, and Experimentation of a Dual Helical Drive Vehicle for Underwater Exploration

  2024-09-23 · 3 citations

  article

  This paper presents the development of a novel underwater screw-propelled vehicle prototype capable of operating both on the surface of the water and underwater. The locomotion of the prototype is controlled by the thrust and buoyancy generated by two helical drives or Archimedes' screws. The thrust generated by the drive depends on its rotational speed while the buoyancy depends on the amount of water in the central cylinder or ballast. Experimentation of the prototype's operation was conducted in a 3.35m-deep diving well at North Carolina State University. Real-time data acquisition and controls were performed through a tethered connection to the vehicle, and video post-processing was used for position estimation. Results from the vehicle's locomotion are presented as it demonstrates a variety of maneuvers including moving along the surface, controlling and maintaining a desired operating depth, and travelling while fully submerged. The experimental data acquired can now be applied towards dynamic model validation in efforts to develop an autonomous underwater screw-propelled vehicle.

  PublisherDOI

Recent grants

• Integrated Structures for Multimode Ambient Energy Harvesting

  NSF · $295k · 2014–2018

• WAKE MEDIATED COUPLING IN OSCILLATING HYDROFOIL TURBINE ARRAYS

  NSF · $346k · 2015–2019

• Control of Aeroelastic Structures via Prescribed Upstream Aerodynamic Disturbances

  NSF · $454k · 2020–2024

• CAREER: Muscle-Inspired Load-Adaptive Actuation for Compliant Robotics

  NSF · $500k · 2019–2024

Frequent coauthors

• Ephrahim Garcia

  Institut Agro Dijon

  31 shared

• Andre P. Mazzoleni

  North Carolina State University

  27 shared

• Ashok Gopalarathnam

  22 shared

• Kenneth Granlund

  19 shared

• Punnag Chatterjee

  Indian Institute of Technology Dharwad

  16 shared

• Edward Chapman

  United States Naval Academy

  15 shared

• Nicholas Mazzoleni

  North Carolina State University

  13 shared

• Sumedh Beknalkar

  North Carolina State University

  12 shared