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Research Overview
My research develops feedback control methods for complex engineering systems, with a focus on rigorous design principles that remain useful when models are uncertain, systems are constrained, and performance requirements are tight.
This page gives a rough thematic map of my research program. For a full publication list, see Papers.
From Theory to Application to Theory to Application to …
In my research, applications inspire new designs, which inspire new theories, which inspire new designs, which enable new applications. It is the tension, the push and pull, between theory and application that drives the type of theoretical research I pursue.
Research Themes
Control Theory and Engineering
Analysis and design tools for feedback systems, with emphasis on stability, robustness, performance, and practicality.
linear, nonlinear, and stochastic control · robust stability · low-gain design
Optimization-Based Control
Control architectures that optimize the dynamic, steady-state, and economic operation of an engineering system.
feedback-based optimization · predictive control · optimal steady-state control
Advanced Power System Control
Power systems are becoming faster, more distributed, and more inverter-dominated. My work develops control tools for reliable operation across transmission, distribution, and microgrid settings.
frequency/voltage control · inverter-based resources · power system operations · hierarchical control
Data-Driven Control
Methods that use data directly for prediction, optimization, and control when models are uncertain or unavailable.
data-driven control · stochastic MPC · tuning regulators
Control Theory and Engineering
This theme focuses on core questions in feedback control: when stability is preserved, how robustness can be certified, and how simple controller structures can be designed with meaningful guarantees. Reliable engineering systems often depend on controllers that are simple enough to implement, but strong enough to tolerate uncertainty, constraints, and nonlinear behaviour. Our goal is often theory that remains close to engineering practice.
Representative papers.
Steady-State Cascade Operators and their Role in Linear Control, Estimation, and Model Reduction Problems. A new viewpoint on linear cascade control, cascade estimation, and model-reduction problems based on simple linear operators. Some nonlinear extensions as well. [PDF] [DOI] [BibTeX]
A Lyapunov Characterization of Robust D-Stability with Application to Decentralized Integral Control of LTI Systems. A necessary and sufficient condition for robust D-stability, immediately applicable to show stability of decentralized integral control. [PDF] [BibTeX]
Low-Gain Stability of Projected Integral Control for Input-Constrained Discrete-Time Nonlinear Systems. A low-gain integral controller which enforces input constraints and is immune to integrator windup. [PDF] [DOI] [BibTeX]
Analysis and Synthesis of Low-Gain Integral Controllers for Nonlinear Systems. A comprehensive framework for analysis and design of low-gain integral controllers. [PDF] [DOI] [BibTeX]
Data-Driven Output Regulation using Single-Gain Tuning Regulators. A multivariable integral-type controller for time-varying reference signals, with design based only on frequency response data. [PDF] [DOI] [BibTeX]
Optimization-Based Control
Formulating control objectives as optimization problems allows us to specify clearly what we want our designs to achieve. I'm interested in designing controllers which optimize the transient performance of systems, and also those which optimize steady-state operation.
Representative papers.
Linear-Convex Optimal Steady-State Control. Provides a framework for controllers that regulate an engineering system while optimizing its steady-state behaviour. [PDF] [DOI] [BibTeX]
Stochastic Data-Driven Predictive Control with Equivalence to Stochastic MPC. Provides a bridge between traditional affine-feedback stochastic predictive control frameworks and recent advances in data-driven control. [PDF] [DOI] [BibTeX]
Low-Gain Stability of Projected Integral Control for Input-Constrained Discrete-Time Nonlinear Systems. An integral controller which enforces convex input constraints and is immune to integrator windup. [PDF] [DOI] [BibTeX]
Low-Gain Stabilizers for Linear-Convex Optimal Steady-State Control. Explicit stabilizing low-gain designs for linear-convex optimal steady-state control. [PDF] [DOI] [BibTeX]
Distributionally Robust Stochastic Data-Driven Predictive Control with Optimized Feedback Gain. Combines data-driven predictive control with distributional robustness and stochastic predictive control. [PDF] [DOI] [BibTeX]
Removing Time-Scale Separation in Feedback-Based Optimization via Estimators. Uses estimators to improve the dynamic performance feedback-based optimization, eliminating the need for time-scale separation. [PDF] [BibTeX]
Advanced Power System Control
Power systems are in a period of rapid technological transition, and advanced control is the essential underlying tool to maintain reliability and high performance in the age of inverter-based resources and active distribution systems. Our technical emphasis is on rigour, simplicity, and reliability.
Representative papers.
Frequency Nadir-Constrained Power System Restoration Planning with Energy Storage. Incorporates frequency-nadir limits into power-system restoration planning with energy storage. [PDF] [BibTeX]
Microgrid Stability Definitions, Analysis, and Examples. Organizes stability definitions, modeling issues, and analysis examples for modern microgrids. [PDF] [DOI] [BibTeX]
A Multi-Area Architecture for Real-Time Feedback-Based Optimization of Distribution Grids. A multi-area hierarchical control architecture to coordinate and optimize DERs in distribution grids, scalable to thousands of devices. [PDF] [DOI] [BibTeX]
Data-Driven Fast Frequency Control using Inverter-Based Resources. Fast frequency controller designs to capitalize on the speed of inverter-based resources; no models, just data. [PDF] [DOI] [BibTeX]
A Theory of Solvability for Lossless Power Flow Equations - Part I and Part II. A deep dive into the power flow equation solution space, including solvability conditions and numerical algorithms with guarantees. [PDF 1] [DOI 1] [BibTeX 1] [PDF 2] [DOI 2] [BibTeX 2]
Data-Driven Control
Building and maintaining accurate dynamic models is difficult and costly. Direct data-driven control designs bypass the model building step, allowing control actions or policies to be directly determined from experimental data. Our focus is on principled methods for designing such controllers, with stability and performance guarantees.
Representative papers.
Stochastic Data-Driven Predictive Control with Equivalence to Stochastic MPC. Provides a bridge between traditional affine-feedback stochastic predictive control frameworks and recent advances in data-driven control. [PDF] [DOI] [BibTeX]
Data-driven harmonic output regulation of a class of nonlinear systems. Tracking and disturbance rejection designs using only data for nonlinear systems. [PDF] [DOI] [BibTeX]
Data-Driven Fast Frequency Control using Inverter-Based Resources. Fast frequency controller designs to capitalize on the speed of inverter-based resources; no models, just data. [PDF] [DOI] [BibTeX]
Distributionally Robust Stochastic Data-Driven Predictive Control with Optimized Feedback Gain. Combines data-driven methods with distributional robustness and stochastic predictive control. [PDF] [DOI] [BibTeX]
Data-Driven Model Predictive Control for Linear Time-Periodic Systems. What the title says. [PDF] [DOI] [BibTeX]
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