1 citations · 2 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
A Scenario-based Model Predictive Control Scheme for Pandemic Response through Non-pharmaceutical Interventions
Domagoj Herceg, Marco DellOro, Riccardo Bertollo +5
This paper presents a scenario-based model predictive control (MPC) scheme designed to control an evolving pandemic via non-pharmaceutical intervention (NPIs). The proposed approac…
A Comparative Study of Distributed Feedback Optimizing Control Architectures
Risvan Dirza, Hari Prasad Varadarajan, Vegard Aas +2
This paper considers the problem of steady-state real-time optimization (RTO) of interconnected systems with a common constraint that couples several units, for example, a shared r…
ECCBO: An Inherently Safe Bayesian Optimization with Embedded Constraint Control for Real-Time Optimization
Dinesh Krishnamoorthy
This paper introduces a model-free real-time optimization (RTO) framework based on unconstrained Bayesian optimization with embedded constraint control. The main contribution lies…
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…
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…
A Bayesian optimization framework for the automatic tuning of MPC-based shared controllers
Anne van der Horst, Bas Meere, Dinesh Krishnamoorthy +5
This paper presents a Bayesian optimization framework for the automatic tuning of shared controllers which are defined as a Model Predictive Control (MPC) problem. The proposed fra…