66 citations · 72 across the 12 of their papers we have counts for
5 papers · 1 filter
Polynomial Chaos-based Stochastic Model Predictive Control: An Overview and Future Research Directions
Prabhat K. Mishra, Joel A. Paulson, Richard D. Braatz
This article is devoted to providing a review of mathematical formulations in which Polynomial Chaos Theory (PCT) has been incorporated into stochastic model predictive control (SM…
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
Formal Certification Methods for Automated Vehicle Safety Assessment
Tong Zhao, Ekim Yurtsever, Joel Paulson +1
Challenges related to automated driving are no longer focused on just the construction of such automated vehicles (AVs), but in assuring the safety of their operation. Recent advan…
A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization
Farshud Sorourifar, Georgios Makrygirgos, Ali Mesbah +1
The closed-loop performance of model predictive controllers (MPCs) is sensitive to the choice of prediction models, controller formulation, and tuning parameters. However, predicti…
Data-Driven Scenario Optimization for Automated Controller Tuning with Probabilistic Performance Guarantees
Joel A. Paulson, Ali Mesbah
Systematic design and verification of advanced control strategies for complex systems under uncertainty largely remains an open problem. Despite the promise of blackbox optimizatio…