55 citations · 99 across the 17 of their papers we have counts for
34 papers · 1 filter
Once upon a time step: A closed-loop approach to robust MPC design
Anilkumar Parsi, Marcell Bartos, Amber Srivastava +2
A novel perspective on the design of robust model predictive control (MPC) methods is presented, whereby closed-loop constraint satisfaction is ensured using recursive feasibility…
Learning-based MPC from Big Data Using Reinforcement Learning
Shambhuraj Sawant, Akhil S Anand, Dirk Reinhardt +1
This paper presents an approach for learning Model Predictive Control (MPC) schemes directly from data using Reinforcement Learning (RL) methods. The state-of-the-art learning meth…
Conflict-free Charging and Real-time Control for an Electric Bus Network
Rémi Lacombe, Nikolce Murgovski, Sébastien Gros +1
The rapid adoption of electric buses by transit agencies around the world is leading to new challenges in the planning and operation of bus networks. In particular, the limited dri…
Equivalence of Optimality Criteria for Markov Decision Process and Model Predictive Control
Arash Bahari Kordabad, Mario Zanon, Sebastien Gros
This paper shows that the optimal policy and value functions of a Markov Decision Process (MDP), either discounted or not, can be captured by a finite-horizon undiscounted Optimal…
Policy Gradient Reinforcement Learning for Uncertain Polytopic LPV Systems based on MHE-MPC
Hossein Nejatbakhsh Esfahani, Sebastien Gros
In this paper, we propose a learning-based Model Predictive Control (MPC) approach for the polytopic Linear Parameter-Varying (LPV) systems with inexact scheduling parameters (as e…
Bridging the gap between QP-based and MPC-based RL
Shambhuraj Sawant, Sebastien Gros
Reinforcement learning methods typically use Deep Neural Networks to approximate the value functions and policies underlying a Markov Decision Process. Unfortunately, DNN-based RL…