6 citations · 6 across the 21 of their papers we have counts for
32 papers
A Koopman--Polynomial Chaos Framework for SMPC of Nonlinear Stochastic Processes
Wallace Gian Yion Tan, Efstathios Iliakis, Liang Wu +2
This paper develops a polynomial parametric Koopman operator (PPKO) framework for stochastic model predictive control (SMPC) of nonlinear systems with parametric uncertainty and ti…
Fast-DeePC: Fast Data-Enabled Predictive Control using Soft-constrained Scheme
Terry Zhang, Jan Drgona, Malika Sader +2
Data-enabled predictive control (DeePC) replaces the explicit system model of model predictive control with a data-driven predictor built directly from recorded input-output trajec…
Model Predictive Control for Safety-Critical Systems Using Taylor's Theorem with Lagrange Remainder
Shuo Liu, Liang Wu, Wei Xiao +2
Safety-critical Model Predictive Control (MPC) formulations based on Control Barrier Functions (CBFs) often require multiple tuning parameters and may become conservative or diffic…
SD-DPC: Sparse Dictionary Differentiable Predictive Control
Ali Reza Daneshvar Garmroodi, Jan Drgoňa
We present sparse dictionary differentiable predictive control (SD-DPC), a framework for learning sparse, interpretable feedback policies for nonlinear systems from data. A predict…
lapanda: A Matrix-Free Differentiable Solver for Nonconvex Constrained Optimization Layers
Yuankun Chen, Zifei Nie, Kangyu Lin +2
Differentiable optimization brings the structural guarantees of mathematical optimization to network pipelines, allowing them to be trained end-to-end. However, its application rem…
Accelerating Branch MPC with Two-Level Parallel Direct Solves on GPUs
Fenglong Song, Luyao Zhang, Liang Wu +2
Branch model predictive control optimizes multiple future trajectories coupled through shared decisions, with computational demands increasing as the number of scenarios and predic…
Safe Receding Horizon Mixed-Integer Differentiable Predictive Control for Degradation-Aware Battery Dispatch
Eshagh Safarzadeh Ravajiri, Jan Drgona, Benjamin F. Hobbs
We present a safe receding-horizon mixed-integer differentiable predictive control methodology for residential battery energy storage dispatch that combines neural-network speed wi…
DR-MPC: Fast and Feasible Dynamics-Relaxed Model-Predictive Control for Legged Locomotion
Run Wang, Alapati Tuerxun, Shuo Liu +4
This paper presents dynamics-relaxed model predictive control (DR-MPC), a novel MPC formulation for legged locomotion, and a tailored interior-point method (IPM) solver. The formul…
Mixed-Integer Nonlinear Differentiable Predictive Control for Underground Pumped Hydro Energy Storage Systems
Honghui Zheng, Ján Boldocký, Yury Dvorkin +1
This paper extends Mixed-Integer Differentiable Predictive Control (MI-DPC) to multi-modal discrete decisions and nonconvex polynomial dynamics arising in Underground Pumped Hydro…
End-to-End Battery Dispatch with Exact Rainflow Degradation via Mixed-Integer Differentiable Predictive Control
Eshagh Safarzadeh Ravajiri, Jan Drgona, Mahdi Mehrtash +1
Optimal dispatch of battery energy storage systems requires balancing energy arbitrage against cycle-induced degradation, which is accurately quantified through rainflow cycle coun…
CINOC: Cardinality-Invariant Neural Operator Policies for Scalable PDE Control
Pietro Zanotta, Dibakar Roy Sarkar, Honghui Zheng +2
Controlling partial differential equations (PDEs) with learning-based policies remains fundamentally limited by fixed-dimensional representations: policies trained for a specific s…
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…
ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs
Weimin Huang, Natalie M. Isenberg, Ján Drgoňa +2
Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization. As solving large-scale combinatorial optimization problems is ch…
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…
Koopman-BoxQP: Solving Large-Scale NMPC at kHz Rates
Liang Wu, Wallace Gian Yion Tan, Richard D. Braatz +1
Solving large-scale nonlinear model predictive control (NMPC) problems at kilohertz (kHz) rates on standard processors remains a formidable challenge. This paper proposes a Koopman…
Time-Certified and Efficient NMPC via Koopman Operator
Liang Wu, Yunhong Che, Bo Yang +2
Certifying and accelerating execution times of nonlinear model predictive control (NMPC) implementations are two core requirements. Execution-time certificate guarantees that the N…
CBDs: Differentiable Causal Block Diagrams
Thomas Beckers, Ján Drgoňa, Truong X. Nghiem
Modern cyber-physical systems (CPS) integrate physics, computation, and learning, demanding modeling frameworks that are simultaneously composable, learnable, and verifiable. Yet e…
Least-Squares Multi-Step Koopman Operator Learning for Model Predictive Control
Liang Wu, Wallace Gian Yion Tan, Leqi Zhou +2
MPC is widely used in real-time applications, but practical implementations are typically restricted to convex QP formulations to ensure fast and certified execution. Koopman-based…
Accelerating Underground Pumped Hydro Energy Storage Scheduling with Decision-Focused Learning
Honghui Zheng, Pietro Favaro, Yury Dvorkin +1
Underground pumped hydro energy storage (UPHES) systems play a critical role in grid-scale energy storage for renewable integration, yet optimal day-ahead scheduling remains comput…
Learning to Control PDEs with Differentiable Predictive Control and Time-Integrated Neural Operators
Dibakar Roy Sarkar, Ján Drgoňa, Somdatta Goswami
We present a data-driven control framework for partial differential equations (PDEs). Our approach integrates time-integrated deep operator networks (TI-DeepONet) as differentiable…