Reid Smith

  • reidds2@illinois.edu
  • Ph.D. Candidate in Mechanical Engineering – University of Illinois – May 2025
  • M.S. Mechanical Engineering – University of Illinois (December 2022)
  • B.S. Mechanical Engineering – Clemson University (December 2019)
  • Research Interests: Model predictive control, iterative learning control, control of electrical-thermal-mechanical systems, control-oriented modeling
  • View Reid Smith's profile on LinkedIn
  • CV

I will be finishing my PhD in Mechanical Engineering at the University of Illinois at Urbana-Champaign in May 2025. I am a NSF GRFP fellow and have conducted research with the NSF Engineering Research Center for Power Optimization of Electro-Thermal Systems (POETS). Working with Dean Andrew Alleyne, my research is in the field of dynamics and control systems with a focus on advanced control strategies for electrical, thermal, and mechanical systems. My current research work is focused on iterative learning control and my past research has focused on model predictive control approaches for improved power and thermal management of interconnected electrical-thermal-mechanical systems.

Published Research Projects

Database-Driven Adaptive Model Predictive Control for Energy Systems

Within aircraft operations such as Urban Air Mobility, there exists repetitiveness in the missions which are flown from day to day. Information from these repetitive flights can be used to improve performance and ensure safe behavior for future flights. Within this work, we use a model predictive controller (MPC) with adaptive properties to ensure an electrified aircraft can prevent constraint violations within the power dynamics while iteratively performing more aggressive flight maneuvers. The terminal state of the MPC is constrained to a safe set which grows throughout each iteration, in addition to online modifications of the cost-to-go function. This database-driven adaptation of the MPC allows for reductions in flight time while preventing safety violations within the flight and power dynamics.

Fig. 1 – Schematic of the electrified aircraft powertrain used within this work.

Iterative Model Predictive Control for Thermal Management Systems

For these repetitive flight operations such as Urban Air Mobility, this repetitiveness can also be used to reduce constraint violations that happen within the thermal management system of aircraft. These constraint violations may be caused by repetitive, challenging sections of flight which stress both the power and thermal dynamics. While controller preview within MPC can aid in reducing these constraint violations, incorporating data from the previous flight(s) can provide information outside of the control horizon for systems with slow dynamics such as thermal systems.

Fig. 2 – Hierarchical control strategy used to adapt the control performance between iterations/flights.

In this work, the slack constraints used within MPC are modified each flight using a simple, closed-form update law. This results in a 97% reduction in thermal constraint violations for the power electronics and vehicle cabin through adaptive performance of the vehicle thermal management system.

Fig. 3 – Iterative thermal performance of the power electronics for the iterative model predictive controller.

Control-Oriented Modeling of Thermal and Mechanical Systems

Turbomachinery such as gas turbine engines and air cycle machines are commonly used within aircraft for propulsion and thermal management. For online, model-based control techniques, control-oriented models of these systems must be created which balance computational efficiency and accuracy. As each of these turbomachinery systems can be represented by a Brayton cycle system, this work develops a control-oriented modeling methodology for Brayton cycle systems. The Brayton cycle system dynamics are compactly represented by a graph-based model which captures the power flows between components. While maintaining comparable accuracy to existing models, the developed approach results in a 98% reduction in computational cost.

Fig. 4 – Schematic of an air cycle machine used within Brayton cycle system modeling.

Graduate Publications – See my Google Scholar for Paper Links

  • Butler, Cary, Smith, Reid, and Alleyne, Andrew. “Sampling-Based Planning for Guaranteed Safe Energy Management of Hybrid UAV Powertrain Under Complex, Uncertain Constraints.” IEEE Transactions on Control Systems Technology. 2024.
  • Smith, Reid, Hencey, Brandon, Parry, Adam, and Alleyne, Andrew. “Learning of Energy Primitives for Electrified Aircraft.” American Control Conference. 2024.
  • Smith, Reid, and Alleyne, Andrew. “Iterative Learning Model Predictive Control for Thermal Management of Urban Air Mobility Vehicles.” Conference on Control Technology and Applications. 2023.
  • Smith, Reid. “Dynamic Modeling and Control of a Hybrid Electric Quadrotor Powertrain and Thermal Management System.” M.S. Thesis. Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign. 2022.
  • Smith, Reid, and Alleyne, Andrew. “Dynamical Graph-Based Models of Brayton Cycle Systems.” American Control Conference. 2022.

Undergraduate Publications

  • Smith, Reid, and Dutta, Sandip. “Conjugate Thermal Optimization with Unsupervised Machine Learning.” ASME Journal of Heat Transfer. 2021.
  • Dutta, Sandip, and Smith, Reid. “Nonlinear Optimization of Turbine Conjugate Heat Transfer with Iterative Machine Learning and Sample Replacement.” Energies. 2020.
  • Dutta, Sandip, and Smith, Reid. “Transfer Function Based Optimization of Film Hole Sizes with Conjugate Heat Transfer Analysis.” ASME Turbomachinery Technical Conference and Exposition. 2020.