About

Kira Barton is a Professor of Mechanical Engineering at the University of Michigan and a Miller Faculty Scholar. Her research interests include control theory and applications, iterative learning control, multi-agent systems, human/robot collaborations, smart manufacturing, manufacturing robotics, and high-performance micro/nano-scale printing for electrical and biomedical applications. She has been recognized as one of the 25 Leaders Transforming Manufacturing by SME in 2022, and her research group received the Manufacturing Leadership Award from the National Association of Manufacturers in 2022. Dr. Barton earned her Ph.D. in Mechanical Engineering from the University of Illinois in 2010, her M.S. from the same institution in 2006, and her B.S. from the University of Colorado in 2001. Her work focuses on improving the functionality of robotic and manufacturing systems through understanding system behavior and developing strategies for performance enhancement. She has received numerous honors, including the ASME DSCD Young Investigator Award in 2017, recognition as a leader in manufacturing by SME, and the NSF CAREER award in 2014. Her contributions span fundamental and experimental modeling and controls research, with a focus on multi-agent coordination and additive manufacturing.

Research topics

• Computer Science
• Engineering
• Artificial Intelligence
• Systems engineering
• Software engineering
• Manufacturing engineering
• World Wide Web
• Industrial engineering
• Database
• Economics

Selected publications

• Characterization and Design Framework for Micro‐ and Nanoscale Printed Metal Interconnects in Hybrid Electronic Systems

  Advanced Materials Technologies · 2026-02-27

  articleOpen accessSenior authorCorresponding

  ABSTRACT High‐resolution printing offers promising avenues for packaging micro‐ and nanoscale modular electrical components, enabling hybrid, high‐performance circuits. The miniaturization of component interfaces imposes stringent requirements on printed interconnect resolution, conductivity, and structural robustness. This work systematically investigates the fabrication and characterization of submicron‐to‐micron scale (300 nm–3 ) metal nanoparticle interconnects, focusing on the interplay between printing parameters, multilayer deposition, and thermal sintering conditions. Silver (Ag) and gold (Au) colloidal inks are printed with controlled cross‐sectional geometries and sintered under dry air, forming gas, and nitrogen atmospheres. Correlations between geometry, sintering atmosphere, and interconnect resistivity reveal that oxygen‐rich and reducing environments promote nanoparticle coalescence, while resistivity and long‐term stability are strongly dependent on cross‐sectional area. Optimized processing yields Au interconnects with widths down to and resistivity of . Integration with micromodular transistor circuits demonstrates that interconnect geometry and device interface quality strongly influence series resistance, and analysis of failure modes identifies strategies to improve reliability. These results establish a framework for designing electrically robust, high‐resolution printed interconnects, enabling reliable integration into next‐generation microelectronic packaging and hybrid interconnection technologies.

  PublisherDOI

• High‐Performance, Paper‐Based Microelectronics via a Micromodular Fabrication Process

  Advanced Materials Interfaces · 2026-03-12

  articleOpen access

  ABSTRACT The integration of high‐performance microelectronics onto flexible, sustainable substrates is important for advancing eco‐friendly technologies. This study presents an approach for creating circuits comprised of silicon micromodular transistors on cellulose nanomaterial (CNM)‐coated paper substrates with silver nanoparticle interconnects formed using electrohydrodynamic jet (e‐jet) printing. The transistors exhibit on/off ratios of ∼10 7 , threshold voltages of 0.00 ± 0.06 V, subthreshold slopes of 102 ± 5 mV/decade, and peak effective mobilities of 430 ± 60 cm 2 V −1 s −1 . Cellulose nanofibril (CNF) and cellulose nanocrystal (CNC) coatings are evaluated for mechanical and electrical performance. CNF‐based coatings offer lower reverse saturation current despite higher surface roughness, while smoother CNC‐based coatings exhibit higher reverse saturation current. Wired micromodular transistors maintain performance under radii of curvature bending strain as small as 2.3 mm. A depletion‐load inverter serves as proof‐of‐concept micromodular circuitry. To the best of our knowledge, this work demonstrates a circuit assembly technique on paper that mitigates many of the trade‐offs of previous paper‐based platforms.

  PublisherDOI

• Swimming Dynamics of Bottlenose Dolphins: a Koopman Modeling Approach

  IFAC-PapersOnLine · 2025-01-01

  articleOpen accessSenior author

  Marine mammals rely on their flukes for propulsion. However, the forces generated by their foil-like flukes can not be measured directly due to the complexities of the marine environment. This study presents a data-driven modeling framework to investigate propulsive hydrodynamic forces during swimming. First, synthetic data was generated using a low-order simulation based on prior research to generate training data for model identification. The simulation models the two-dimensional translational motion (longitudinal and vertical) of the animal and approximates its fluking gait as a multi-linkage system. The propulsion force acting on the fluke is simulated using the principles of unsteady hydrodynamics and hydroelasticity. Subsequently, extended dynamic mode decomposition identifies a nonlinear model by lifting the original state-space into a higher-order nonlinear representation. The results demonstrate that the proposed method accurately estimates both the motion of the animal and the hydrodynamic forces exerted on it.

  PublisherDOI

• Hierarchical Sensor-Robot Control for On-Demand Sensing in a Partially Known Environment

  2025-08-17

  articleSenior author

  To enable industrial robot autonomy without traditional manual programming, current approaches involve a carefully modeled environment or dedicated sensor feedback. This paper explores a novel alternative regime: on-demand sensing, in which a fleet of sensorless robots operating in un-modeled environments adapt to frequently changing repetitive tasks by requesting temporary access to a shared mobile sensor. A Hierarchical Sensor-Robot Control scheme is developed to enable an ad hoc team to cooperatively solve a task, at which point the sensor is dismissed while the robot repeats the task safely in open loop. An outer loop simultaneously optimizes the sensor pose and the parameters of an inner loop robot controller, which is encoded with potential fields. Simulation results demonstrate the algorithm converging for a realistic problem after just three outer-loop iterations.

  PublisherDOI

• Iterative Learning-Based Arbitration in Shared Vehicle Control: Methodological Formulation and Driver-in-the-Loop Results

  2025-08-25

  article

  Shared control, which merges the dynamic inputs of human drivers with vehicle automation, has attracted considerable attention due to its potential to enhance both safety and driver satisfaction. However, most existing shared control strategies are based on one-size-fits-all designs, neglecting the fact that the optimal level of sharing will typically depend on the individual driver and road characteristics. In light of these limitations, along with the observation that routes are commonly repeated, we propose an iterative learning control algorithm that divides a closed-loop driving circuit into discrete segments, enabling online estimation of the driver's performance over each segment. By adapting arbitration weights based on these driver-specific and segment-specific estimates, our method seeks to reduce lateral tracking errors, reduce path completion time, and increase secondary task performance. To validate this approach, we conducted driver-in-the-loop simulator tests with 20 participants, each driving repeatedly on the closed-loop circuit. The results demonstrate that our personalized strategy significantly improves driving performance.

  PublisherDOI

• Iterative Input Shaping for Line Width Robustness in Additive Manufacturing

  IFAC-PapersOnLine · 2025-01-01

  articleOpen accessSenior authorCorresponding

  Micro-additive manufacturing describes a broad domain in 3D printing used to fabricate high-resolution patterns for printed electronics, biosensors, and labels. Material jetting, a process in which ink is printed and interacts with the surface in liquid form, is commonly used to make printed electronics. Despite advantages in material diversity and drop-on-demand capabilities, deviations in the volumes of the printed droplets can lead to poor device performance. Real-time feedback control is often infeasible due to fast jetting dynamics and high-resolution feature sizes. In this work, we consider iterative methods to address limitations in real-time monitoring and control actuation. Iterative model updating in the form of a piecewise linear approximation and nonlinear curve fitting is used to derive updated models of the process to enable feedforward parameter selections for subsequent patterns. A comparison with traditional model-based ILC is incorporated and various error metrics are reported. Among the standard and incrementally variable experimental conditions, the results indicate that the proposed approaches offer faster convergence and a significant reduction in transient error when transitioning between different widths for the reference line.

  PublisherDOI

• Automated desktop wiring of micromodular electronic systems with submicron electrohydrodynamic jet printed interconnects

  Additive manufacturing · 2025-06-16 · 3 citations

  articleSenior authorCorresponding
  PublisherDOI

• Characterization of Human Driving Behaviors in Shared Vehicle Control Based on Level-k Cognitive Modeling

  2025-07-08

  articleSenior author

  This paper presents how game-theoretic level-k cognitive modeling, a framework for modeling the decision-making of agents with bounded rationality, can be used to describe the interactive policies within human-autonomy teams during collaborative tasks. The approach hypothesizes that prediction of human behavior can be used to enable autonomous systems to make better decisions. A case study is used to create a preliminary data library mapping human behavior to specific level-k strategies. Specifically, a participant completed laps on a shared control driving simulator while performing a secondary task. Between laps, the participant is informed of changes in the vehicle controller that impact the influence of the human input on the vehicle motion, reflecting changes in the belief the human holds about the autonomy, or changing their level-k. Using the library, the mapping between human behavior and level-k theory is evaluated through the classification of interactions between human-autonomy teams under directed scenarios.

  PublisherDOI

• Influence of Explicit Instruction on the Mechanisms underlying Neuromuscular Admittance

  IFAC-PapersOnLine · 2025-01-01

  articleOpen accessCorresponding

  The determinants of human response to force perturbation include non-volitional mechanisms, such as biomechanics and reflex responses, as well as volitional means such as co-contraction and cognitive responses to haptic sensory feedback. Thus the modulation of neuromuscular admittance is receptive to instructions regarding these volitional strategies, even when the task remains constant. In this paper we investigate the influence of instruction on neuromuscular admittance when participants attempted to maintain the position of a manual control interface while experiencing unpredictable force perturbations. We found a clear distinction between trials where (N = 10) participants were instructed to stiffen their arm by co-contracting or respond to forces they felt by producing opposing forces. Our findings have implications on the design of shared control strategies between humans and automation.

  PublisherDOI

• Digital Twin-Based Smart Manufacturing: Dynamic Line Reconfiguration for Disturbance Handling

  IEEE Transactions on Automation Science and Engineering · 2025-01-01 · 7 citations

  articleOpen accessSenior author

  The increasing complexity of modern manufacturing, coupled with demand fluctuation, supply chain uncertainties, and product customization, underscores the need for manufacturing systems that can flexibly update their configurations and swiftly adapt to disturbances. However, current research falls short in providing a holistic reconfigurable manufacturing framework that seamlessly monitors system disturbances, optimizes alternative line configurations based on machine capabilities, and automates simulation evaluation for swift adaptations. This paper presents a dynamic manufacturing line reconfiguration framework to handle disturbances that result in operation time changes. The framework incorporates a system process digital twin for monitoring disturbances and triggering reconfigurations, a capability-based ontology model capturing available agent and resource options, a configuration optimizer generating optimal line configurations, and a simulation generation program initializing simulation setups and evaluating line configurations at approximately 400x real-time speed. A case study of a battery production line has been conducted to evaluate the proposed framework. In two implemented disturbance scenarios, the framework successfully recovers system throughput with limited resources, preventing the 26% and 63% throughput drops that would have occurred without a reconfiguration plan. The reconfiguration optimizer efficiently finds optimal solutions, taking an average of 0.03 seconds to find a reconfiguration plan for a manufacturing line with 51 operations and 40 available agents across 8 agent types.

  PublisherOA PDFDOI

Recent grants

• CPS: TTP Option: Frontiers: Collaborative Research: Software Defined Control for Smart Manufacturing Systems

  NSF · $2.4M · 2016–2022

• A Model-Based Intelligent Agent Approach for Supply Chain Transparency and Resilience

  NSF · $409k · 2021–2025

• High Fidelity Additive Manufacturing at the Micro-scale

  NSF · $300k · 2013–2017

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

  NSF · $212k · 2017–2021

• EAGER/Collaborative Research/Cyber Manufacturing: Cyber-Manufacturing Systems for Open Product Realization

  NSF · $215k · 2015–2020

Frequent coauthors

• Dawn M. Tilbury

  University of Michigan–Ann Arbor

  96 shared

• James Moyne

  University of Michigan–Ann Arbor

  37 shared

• David J. Hoelzle

  37 shared

• K. Alex Shorter

  University of Michigan–Ann Arbor

  37 shared

• Efe C. Balta

  32 shared

• Andrew G. Alleyne

  University of Minnesota

  27 shared

• Ilya Kovalenko

  Pennsylvania State University

  26 shared

• Isaac A. Spiegel

  Delft University of Technology

  19 shared

Education

• PhD, Mechanical Engineering

  University of Illinois: Urbana-Champaign

  2010

• Masters of Science, Mechanical Engineering

  University of Illinois: Urbana-Champaign

  2006

• Bachelor of Science, Mechanical Engineering

  University of Colorado Boulder

  2001

Awards & honors

• Recognized by SME as one of the 25 Leaders Transforming Manu…
• Lemelson-MIT Illinois Student Finalist, University of Illino…
• ASME Graduate Teaching Fellowship, University of Illinois, 2…
• IREE NSF Travel Grant, Technical University of Eindhoven, Th…
• Manufacturing Leadership Award, The National Association of…