activity
20192022
most citedModel Predictive Control Tuning by Monte Carlo Simulation and Controller Matching

1 citations · 1 across the 5 of their papers we have counts for

collaborators

12 papers

eess.SY20221 cited

Model Predictive Control Tuning by Monte Carlo Simulation and Controller Matching

Morten Ryberg Wahlgreen, John Bagterp Jørgensen, Mario Zanon

This paper presents a systematic method for the selection of the Model Predictive Control (MPC) stage cost. We match the MPC feedback law to a proportional-integral (PI) controller…

eess.SY2021

Model Predictive Control with Infeasible Reference Trajectories

Ivo Batkovic, Mohammad Ali, Paolo Falcone +1

Model Predictive Control (MPC) formulations are typically built on the requirement that a feasible reference trajectory is available. In practical settings, however, references tha…

cs.LG2021

Stability-Constrained Markov Decision Processes Using MPC

Mario Zanon, Sébastien Gros, Michele Palladino

In this paper, we consider solving discounted Markov Decision Processes (MDPs) under the constraint that the resulting policy is stabilizing. In practice MDPs are solved based on s…

physics.data-an2021

Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraints

Nicolò Vallarano, Matteo Bruno, Emiliano Marchese +5

Exponential Random Graph Models (ERGMs) have gained increasing popularity over the years. Rooted into statistical physics, the ERGMs framework has been successfully employed for re…

eess.SY2020

Primal or Dual Terminal Constraints in Economic MPC? -- Comparison and Insights

Timm Faulwasser, Mario Zanon

This chapter compares different formulations for Economic nonlinear Model Predictive Control (EMPC) which are all based on an established dissipativity assumption on the underlying…

eess.SY2020

A Gauss-Newton-Like Hessian Approximation for Economic NMPC

Mario Zanon

Economic Model Predictive Control (EMPC) has recently become popular because of its ability to control constrained nonlinear systems while explicitly optimizing a prescribed perfor…