fbpx Huayi Li | University of Kentucky College of Engineering


207 Crounse Hall, 4810 Alben Barkley Drive, Paducah



2023-present Assistant Professor, University of Kentucky

2022-2023 Postdoctoral Researcher, Texas A&M University

2015-2018 Engineer, Toyota Motor North America


Ph.D. Aerospace Engineering, University of Michigan, Ann Arbor, 2022

M.S. Automotive Engineering, Clemson University, 2015

B.S. Automotive Engineering, China Agricultural University, 2013


My research focuses on constrained and optimal controls for mobility applications, such as connected and autonomous driving, by applying game-theoretic and set-theoretic techniques. I also teach in the field of autonomous systems and control.

Current Projects

Fault-Tolerant Control for Safety-Critical Mobility Systems
Set-Theoretic Fault-Tolerant Control for Aircraft Longitudinal Flight
Fault-tolerant control (FTC) for safety-critical applications, e.g., aircraft flight control, where a reference governor-centric framework with a set membership-based offline design is developed to guarantee the online feasibility of safe operation, failure isolation, and system reconfiguration while tracking references.

  •  M. Castroviejo-Fernandez, H. Li, A. Cotorruelo, E. Garone, and I. Kolmanovsky, "Less conservative robust reference governors and their applications," European Journal of Control, under review.
  •  H. Li, I. Kolmanovsky, and A. Girard, "Integrating failure detection and isolation into a reference governor-based reconfiguration strategy for stuck actuators," in 2022 American Control Conference (ACC), pp. 4311-4316, IEEE, 2022.
  •  H. Li, I. Kolmanovsky, and A. Girard, "Set-theoretic failure mode reconfiguration for stuck actuators," IEEE Control Systems Letters, vol. 6, pp. 1316-1321, 2021.
  •  H. Li, I. Kolmanovsky, and A. Girard, "A failure mode reconfiguration strategy based on constraint admissible and recoverable sets," in 2021 American Control Conference (ACC), pp. 4771-4776, IEEE, 2021.

Motion Planning and Control for Connected and Autonomous Vehicles
Safe and energy-aware autonomous driving, where the training of the reinforcement learning (RL) controller is facilitated by a level-k game-theoretic shared-traffic simulator augmented with an electric vehicle (EV) energy model.

  •  H. Li, N. Li, I. Kolmanovsky, and A. Girard, "Energy-efficient autonomous driving using cognitive driver behavioral models and reinforcement learning," in AI-enabled Technologies for Autonomous and Connected Vehicles, pp. 283-305, Springer, 2022.
  •  H. Li, N. Li, I. Kolmanovsky, and A. Girard, "Energy-efficient autonomous vehicle control using reinforcement learning and interactive traffic simulations," in 2020 American Control Conference (ACC), pp. 3029-3034, IEEE, 2020.

Guaranteed-Safe Overtaking on Rural Highways
Motion planning and control for autonomous driving with guaranteed safe overtaking on two-lane rural highways using trajectory optimization and (set-theoretic) reference governor.

  •  H. Li, F. Assadian, and R. Langari, "A reference governor-based autonomous driving control strategy for guaranteed safe overtaking," IEEE Transactions on Intelligent Transportation Systems, under review.

Past Projects

Control-Oriented Engine Modeling
Development of diesel engine air path and NOx, soot, and HC emission models as plant models to facilitate research of advanced control techniques.

  •  H. Li, K. Butts, K. Zaseck, D. Liao-McPherson, and I. Kolmanovsky, "Emissions modeling of a light-duty diesel engine for model-based control design using multi-layer perceptron neural networks," tech. rep., SAE Technical Paper, 2017.

Hybrid Electric Vehicle Powertrain Configuration Analysis
Development of a powertrain simulation platform with optimal control-based energy management systems (EMS) to analyze fuel economy, acceleration performance, and longitudinal riding comfort.

Research Interests

Constrained and optimal control; Fault-tolerant control; Motion planning and control for connected and autonomous vehicles; Control-oriented modeling of traffic and vehicle systems; Safe, clean, and energy-efficient mobility.


I believe it is essential that student's interest in learning continues growing as the course progresses. Besides, students in engineering must be able to effectively implement the knowledge they learn in class to address practical problems. To fulfill this philosophy, I incorporate active learning and inclusive teaching in my classroom.

University of Kentucky, Paducah Campus
AER 545 - Aircraft Control and Simulation | Undergraduate, Graduate | Spring 2024
This course covers advanced topics in dynamics and control of atmospheric flight vehicles. Major topics include six-degrees-of-freedom kinematic representations of aircraft motion, aerodynamic force modeling, aircraft equations of motion, flight stability and performance, and flight control design.

ME 440 - Design of Control Systems | Undergraduate | Fall 2023, Fall 2024
Fundamentals of classical control theory. Mathematical representation of feedback control systems using block diagrams and transfer functions. Design and analysis of feedback control systems using root-locus, Nyquist, and Bode methods to ensure system stability and meet desired system response specifications. Numerical simulation of feedback control systems.

ME 340 - Introduction to Mechanical Systems | Undergraduate | Spring 2024
Modeling of mechanical, thermal, hydraulic and electrical systems, and other phenomena from a systems viewpoint. Analysis of continuous-time models for free and forced response. Laplace transforms and transfer functions. Introduction to numerical simulation. Analysis of higher-order systems.

EM 313 - Dynamics | Undergraduate | Fall 2024
Study of the motion of bodies. Kinematics: cartesian and polar coordinate systems; normal and tangential components; translating and rotating reference frames. Kinetics of particles and rigid bodies: laws of motion; work and energy; impulse and momentum.

University of Michigan, Ann Arbor
AEROSP - 575 Flight and Trajectory Optimization | Graduate | Winter 2020 (Guest Lecturer, Online)
Formulation and solution of optimization problems for atmospheric flight vehicles and space flight vehicles. Optimality criteria, constraints, vehicle dynamics. Flight and trajectory optimization as problems of nonlinear programming, calculus of variations and optimal control. Algorithms and software for solution of flight and trajectory optimization problems.

AEROSP - 470 Control of Aerospace Vehicles | Undergraduate | Fall 2019 (Graduate Student Instructor)
Foundations of classical control theory; introduction to observers and state space control theory; effect of nonlinearities; application to aircraft and spacecraft; simulation of control systems using relevant software.


If you're interested in joining, I'd love to hear from you!

Openings: 2 (updated May 2024)
Please email your CV with a summary showcasing your background in modeling and control for future mobility.
Students with the following experience are strongly encouraged to apply.

  • Math courses: linear algebra, real analysis, numerical analysis
  • Engineering courses: modeling and analysis of dynamic systems, control theory
  • Operating system: Ubuntu Linux
  • Programming languages: Python and/or C++
  • Software & toolboxes: Matlab/Simulink, CasADi, Robot Operating System (ROS)
  • Hardware: Raspberry Pi

Current UK Paducah students, please reach out if you're interested in research on mobility systems and control.