16 papers
Stability Enhanced Gaussian Process Variational Autoencoders
Carl R. Richardson, Jichen Zhang, Ethan King +1
A novel stability-enhanced Gaussian process variational autoencoder (SEGP-VAE) is proposed for indirectly training a low-dimensional linear time invariant (LTI) system, using high-…
Fixed-time-stable ODE Representation of Lasso
Liang Wu, Yunhong Che, Wallace Gian Yion Tan +3
Lasso problems arise in many areas, including signal processing, machine learning, and control, and are closely connected to sparse coding mechanisms observed in neuroscience. A co…
Polynomial Parametric Koopman Operators for Stochastic MPC
Efstathios Iliakis, Wallace Gian Yion Tan, Liang Wu +2
This paper develops a parametric Koopman operator framework for Stochastic Model Predictive Control (SMPC), where the Koopman operator is parametrized by Polynomial Chaos Expansion…
SCORE: Statistical Certification of Regions of Attraction via Extreme Value Theory
Pietro Zanotta, Panos Stinis, Ján Drgoňa
Certifying the Region of Attraction (ROA) for high-dimensional nonlinear dynamical systems remains a severe computational bottleneck. Traditional deterministic verification methods…
Data Center Chiller Plant Optimization via Mixed-Integer Nonlinear Differentiable Predictive Control
Ján Boldocký, Cary Faulkner, Elad Michael +3
We present a computationally tractable framework for real-time predictive control of multi-chiller plants that involve both discrete and continuous control decisions coupled throug…
L2O-CCG: Adversarial Learning with Set Generalization for Adaptive Robust Optimization
Zhiyi Zhou, Ján Drgoňa, Yury Dvorkin
The adversarial subproblem in two-stage adaptive robust optimization (ARO), which identifies the worst-case uncertainty realization, is a major computational bottleneck. This diffi…