46 citations · 414 across the 52 of their papers we have counts for
22 papers · 1 filter
A Dynamic Penalty Function Approach for Constraints-Handling in Reinforcement Learning
Haeun Yoo, Victor M. Zavala, Jay H. Lee
Reinforcement learning (RL) is attracting attention as an effective way to solve sequential optimization problems that involve high dimensional state/action space and stochastic un…
Mitigating Investment Risk Using Modular Technologies
Yue Shao, Yicheng Hu, Victor M. Zavala
We study logistical investment flexibility provided by modular processing technologies for mitigating risk. Specifically, we propose a multi-stage stochastic programming formulatio…
SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization
Paul F. Lang, Sungho Shin, Victor M. Zavala
Motivation: Estimating model parameters from experimental observations is one of the key challenges in systems biology and can be computationally very expensive. While the Julia pr…
Graph-Based Modeling and Decomposition of Energy Infrastructures
Sungho Shin, Carleton Coffrin, Kaarthik Sundar +1
Nonlinear optimization problems are found at the heart of real-time operations of critical infrastructures. These problems are computationally challenging because they embed comple…
MPC Controller Tuning using Bayesian Optimization Techniques
Qiugang Lu, Ranjeet Kumar, Victor M. Zavala
We present a Bayesian optimization (BO) framework for tuning model predictive controllers (MPC) of central heating, ventilation, and air conditioning (HVAC) plants. This approach t…
Dual Dynamic Programming for Multi-Scale Mixed-Integer MPC
Ranjeet Kumar, Michael J. Wenzel, Mohammad N. ElBsat +3
We propose a dual dynamic integer programming (DDIP) framework for solving multi-scale mixed-integer model predictive control (MPC) problems. Such problems arise in applications th…
Economic Properties of Multi-Product Supply Chains
Philip A. Tominac, Victor M. Zavala
We interpret multi-product supply chains (SCs) as coordinated markets; under this interpretation, a SC optimization problem is a market clearing problem that allocates resources an…
A Graph-Based Modeling Abstraction for Optimization: Concepts and Implementation in Plasmo.jl
Jordan Jalving, Sungho Shin, Victor M. Zavala
We present a general graph-based modeling abstraction for optimization that we call an OptiGraph. Under this abstraction, any optimization problem is treated as a hierarchical hype…
Image-Based Model Predictive Control via Dynamic Mode Decomposition
Qiugang Lu, Victor M. Zavala
We present a data-driven model predictive control (MPC) framework for systems with high state-space dimensionalities. This work is motivated by the need to exploit sensor data that…
Topological Data Analysis: Concepts, Computation, and Applications in Chemical Engineering
Alexander D. Smith, Pawel Dlotko, Victor M. Zavala
A primary hypothesis that drives scientific and engineering studies is that data has structure. The dominant paradigms for describing such structure are statistics (e.g., moments,…
On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control
Sen Na, Sungho Shin, Mihai Anitescu +1
We study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time domai…
Multiscale Model Predictive Control of Battery Systems for Frequency Regulation Markets using Physics-Based Models
Yankai Cao, Seong Beom Lee, Venkat R. Subramanian +1
We propose a multiscale model predictive control (MPC) framework for stationary battery systems that exploits high-fidelity models to trade-off short-term economic incentives provi…
A Sigmoidal Approximation for Chance-Constrained Nonlinear Programs
Yankai Cao, Victor M. Zavala
We propose a sigmoidal approximation for the value-at-risk (that we call SigVaR) and we use this approximation to tackle nonlinear programs (NLPs) with chance constraints. We prove…
Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control
Qiugang Lu, Sungho Shin, Victor M. Zavala
Dynamic mode decomposition (DMD) is a versatile approach that enables the construction of low-order models from data. Controller design tasks based on such models require estimates…
Unifying Theorems for Subspace Identification and Dynamic Mode Decomposition
Sungho Shin, Qiugang Lu, Victor M. Zavala
This paper presents unifying results for subspace identification (SID) and dynamic mode decomposition (DMD) for autonomous dynamical systems. We observe that SID seeks to solve an…
On the Convergence of the Dynamic Inner PCA Algorithm
Sungho Shin, Alex D. Smith, S. Joe Qin +1
Dynamic inner principal component analysis (DiPCA) is a powerful method for the analysis of time-dependent multivariate data. DiPCA extracts dynamic latent variables that capture t…
Overlapping Schwarz Decomposition for Constrained Quadratic Programs
Sungho Shin, Mihai Anitescu, Victor M. Zavala
We present an overlapping Schwarz decomposition algorithm for constrained quadratic programs (QPs). Schwarz algorithms have been traditionally used to solve linear algebra systems…
Multi-Grid Schemes for Multi-Scale Coordination of Energy Systems
Sungho Shin, Victor M. Zavala
We discuss how multi-grid computing schemes can be used to design hierarchical coordination architectures for energy systems. These hierarchical architectures can be used to manage…
Stochastic Model Predictive Control for Central HVAC Plants
Ranjeet Kumar, Michael J. Wenzel, Mohammad N. ElBsat +3
We present a stochastic model predictive control (MPC) framework for central heating, ventilation, and air conditioning (HVAC) plants. The framework uses real data to forecast and…
Computing Economic-Optimal and Stable Equilibria for Droop-Controlled Microgrids
Sungho Shin, Victor M. Zavala
We consider the problem of computing equilibria (steady-states) for droop-controlled, islanded, AC microgrids that are both economic-optimal and dynamically stable. This work is mo…
A Hierarchical Optimization Architecture for Large-Scale Power Networks
Sungho Shin, Philip Hart, Thomas Jahns +1
We present a hierarchical optimization architecture for large-scale power networks that overcomes limitations of fully centralized and fully decentralized architectures. The archit…
Diffusing-Horizon Model Predictive Control
Sungho Shin, Victor M. Zavala
We analyze a time-coarsening strategy for model predictive control (MPC) that we call diffusing-horizon MPC. This strategy seeks to overcome the computational challenges associated…