activity
20232026
most citedLearning the cost-to-go for mixed-integer nonlinear model predictive control

1 citations · 2 across the 6 of their papers we have counts for

collaborators

6 papers

eess.SY2026

Learning myopic mixed-integer nonlinear model predictive control from expert demonstrations

Christopher Anthony Orrico, W. P. M. H. Heemels, Dinesh Krishnamoorthy

Applying nonlinear model predictive control (NMPC) to systems with hybrid dynamics or discrete actions typically yields mixed-integer nonlinear programs (MINLPs), whose real-time s…

eess.SY2025

MPC strategies for density profile control with pellet fueling in nuclear fusion tokamaks under uncertainty

Christopher A. Orrico, Hari Prasad Varadarajan, Matthijs van Berkel +4

Control of the density profile based on pellet fueling for the ITER nuclear fusion tokamak involves a multi-rate nonlinear system with safety-critical constraints, input delays, an…

math.OC2025

Value Function Approximation for Nonlinear MPC: Learning a Terminal Cost Function with a Descent Property

T. M. J. T. Baltussen, C. A. Orrico, A. Katriniok +2

We present a novel method to synthesize a terminal cost function for a nonlinear model predictive controller (MPC) through value function approximation using supervised learning. E…

eess.SY2024★ 1 cited

Learning the cost-to-go for mixed-integer nonlinear model predictive control

Christopher A. Orrico, W. P. M. H. Heemels, Dinesh Krishnamoorthy

Application of nonlinear model predictive control (NMPC) to problems with hybrid dynamical systems, disjoint constraints, or discrete controls often results in mixed-integer formul…

cs.LG2024★ 1 cited

On Building Myopic MPC Policies using Supervised Learning

Christopher A. Orrico, Bokan Yang, Dinesh Krishnamoorthy

The application of supervised learning techniques in combination with model predictive control (MPC) has recently generated significant interest, particularly in the area of approx…

eess.SY2023

Mixed-Integer MPC Strategies for Fueling and Density Control in Fusion Tokamaks

Christopher A. Orrico, Matthijs van Berkel, Thomas O. S. J. Bosman +2

Model predictive control (MPC) is promising for fueling and core density feedback control in nuclear fusion tokamaks, where the primary actuators, frozen hydrogen fuel pellets fire…