BASc (University of Toronto), PhD (University of Michigan), Postdoc (ETH Zürich)
My research is in the area of algorithmic control and lies at the interface of optimization, control and computing. I design sophisticated real-time computational decision-making and data-processing algorithms and study how they interact with the physical systems they control. My current research interests include:
- Predictive and Constrained Control
- Multi-agent Systems
- Algorithmic Game Theory
- Real-time, Embedded, and Distributed Optimization
- Energy Systems, Manufacturing, and Robotics
Current Research Work
- Predictive and Constrained Control: All systems are constrained, e.g., aircraft are subject to angle-of-attack constraints to prevent stall, autonomous vehicles must avoid obstacles, and electric motors have torque and power limits. I’m interested in developing control algorithms that account for these constraints in a systematic and optimal manner. In particular, I work extensively on model predictive control and reference governors.
- Real-time Optimization: Solving optimization problems in real-time and in resource constrained environments is a critical for enabling complex goal-oriented behaviors in autonomous systems. I’m interested in developing optimization algorithms that are suited for deployment on embedded computers as well as distributed algorithms for multi-agent systems with limited communication capabilities.
- Game-theoretic Control: Many critical engineering systems such as energy grids, traffic networks, or supply chains are made up of multiple interacting subsystems controlled by various human or automated agents. These agents rarely agree on system-wide objectives but still need to coordinate to manage shared resources and infrastructure (e.g., power generation, production capacity, or transit links). I am interested in developing coordination and control mechanisms that enable the safe coexistence of agents with conflicting objectives using tools from control and game theory.
- Applications in Energy, Manufacturing, and Robotics: My research is applicable to a wide variety of application domains. I’m currently interested in applying optimization-based and game-theoretic control to additive manufacturing (3D-printing) processes, smart grids and buildings, precision motion systems, supply chains/logistic networks, and multi-agent planning for autonomous vehicles. In the past, I’ve worked on engine emissions control, landing spacecraft on asteroids, and upset recovery in aircraft.
- Liao-McPherson, Dominic, Marco M. Nicotra, and Ilya Kolmanovsky. “Time-distributed optimization for real-time model predictive control: Stability, robustness, and constraint satisfaction.” Automatica117 (2020): 108973.
- Liao-McPherson, Dominic, and Ilya Kolmanovsky. “FBstab: A proximally stabilized semismooth algorithm for convex quadratic programming.” Automatica113 (2020): 108801.
- Liao‐McPherson, Dominic, et al. “Model predictive emissions control of a diesel engine airpath: Design and experimental evaluation.” International Journal of Robust and Nonlinear Control17 (2020): 7446-7477.
- Belgioioso, Giuseppe, Dominic Liao-McPherson, Mathias Hudoba de Badyn, Saverio Bolognani, John Lygeros, and Florian Dörfler. “Sampled-data online feedback equilibrium seeking: Stability and tracking.” In 2021 60th IEEE Conference on Decision and Control (CDC), pp. 2702-2708. IEEE, 2021.
- Liao-McPherson, Dominic, et al. “On Robustness in Optimization-Based Constrained Iterative Learning Control.” IEEE Control Systems Letters, vol. 6, pp. 2846–2851, 2022,
For a full list of publications, visit my profile on: