341 citations · 793 across the 35 of their papers we have counts for
43 papers
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…
Deep active learning for nonlinear system identification
Erlend Torje Berg Lundby, Adil Rasheed, Ivar Johan Halvorsen +3
The exploding research interest for neural networks in modeling nonlinear dynamical systems is largely explained by the networks' capacity to model complex input-output relations d…
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…
Solving Mission-Wide Chance-Constrained Optimal Control Using Dynamic Programming
Kai Wang, Sebastien Gros
This paper aims to provide a Dynamic Programming (DP) approach to solve the Mission-Wide Chance-Constrained Optimal Control Problems (MWCC-OCP). The mission-wide chance constraint…
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…
Interpretable Battery Cycle Life Range Prediction Using Early Degradation Data at Cell Level
Huang Zhang, Yang Su, Faisal Altaf +2
Battery cycle life prediction using early degradation data has many potential applications throughout the battery product life cycle. For that reason, various data-driven methods h…
Functional Stability of Discounted Markov Decision Processes Using Economic MPC Dissipativity Theory
Arash Bahari Kordabad, Sebastien Gros
This paper discusses the functional stability of closed-loop Markov Chains under optimal policies resulting from a discounted optimality criterion, forming Markov Decision Processe…
Quasi-Newton Iteration in Deterministic Policy Gradient
Arash Bahari Kordabad, Hossein Nejatbakhsh Esfahani, Wenqi Cai +1
This paper presents a model-free approximation for the Hessian of the performance of deterministic policies to use in the context of Reinforcement Learning based on Quasi-Newton st…
Backstepping-based Integral Sliding Mode Control with Time Delay Estimation for Autonomous Underwater Vehicles
Hossein Nejatbakhsh Esfahani, Behdad Aminian, Esten Ingar Grøtli +1
The aim of this paper is to propose a high performance control approach for trajectory tracking of Autonomous Underwater Vehicles (AUVs). However, the controller performance can be…
A Semi-Distributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections
Robert Hult, Mario Zanon, Sebastien Gros +1
In this paper, we consider the optimal coordination of automated vehicles at intersections under fixed crossing orders. We formulate the problem using direct optimal control and ex…
Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning
Eivind Bøhn, Sebastien Gros, Signe Moe +1
Model predictive control (MPC) is increasingly being considered for control of fast systems and embedded applications. However, the MPC has some significant challenges for such sys…
Recursive Feasibility of Stochastic Model Predictive Control with Mission-Wide Probabilistic Constraints
Kai Wang, Sebastien Gros
This paper is concerned with solving chance-constrained finite-horizon optimal control problems, with a particular focus on the recursive feasibility issue of stochastic model pred…
Optimal Management of the Peak Power Penalty for Smart Grids Using MPC-based Reinforcement Learning
Wenqi Cai, Hossein N. Esfahani, Arash B. Kordabad +1
The cost of the power distribution infrastructures is driven by the peak power encountered in the system. Therefore, the distribution network operators consider billing consumers b…
Multi-agent Battery Storage Management using MPC-based Reinforcement Learning
A. Bahari Kordabad, W. Cai, S. Gros
In this paper, we present the use of Model Predictive Control (MPC) based on Reinforcement Learning (RL) to find the optimal policy for a multi-agent battery storage system. A time…
A New Dissipativity Condition for Asymptotic Stability of Discounted Economic MPC
Mario Zanon, Sébastien Gros
Economic Model Predictive Control has recently gained popularity due to its ability to directly optimize a given performance criterion, while enforcing constraint satisfaction for…
MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehicles
Wenqi Cai, Arash B. Kordabad, Hossein N. Esfahani +2
In this work, we propose a Model Predictive Control (MPC)-based Reinforcement Learning (RL) method for Autonomous Surface Vehicles (ASVs). The objective is to find an optimal polic…
Verification of Dissipativity and Evaluation of Storage Function in Economic Nonlinear MPC using Q-Learning
Arash Bahari Kordabad, Sebastien Gros
In the Economic Nonlinear Model Predictive (ENMPC) context, closed-loop stability relates to the existence of a storage function satisfying a dissipation inequality. Finding the st…
Approximate Robust NMPC using Reinforcement Learning
Hossein Nejatbakhsh Esfahani, Arash Bahari Kordabad, Sebastien Gros
We present a Reinforcement Learning-based Robust Nonlinear Model Predictive Control (RL-RNMPC) framework for controlling nonlinear systems in the presence of disturbances and uncer…
Bias Correction in Deterministic Policy Gradient Using Robust MPC
Arash Bahari Kordabad, Hossein Nejatbakhsh Esfahani, Sebastien Gros
In this paper, we discuss the deterministic policy gradient using the Actor-Critic methods based on the linear compatible advantage function approximator, where the input spaces ar…
MPC-based Reinforcement Learning for Economic Problems with Application to Battery Storage
Arash Bahari Kordabad, Wenqi Cai, Sebastien Gros
In this paper, we are interested in optimal control problems with purely economic costs, which often yield optimal policies having a (nearly) bang-bang structure. We focus on polic…
Economic MPC of Markov Decision Processes: Dissipativity in Undiscounted Infinite-Horizon Optimal Control
Sébastien Gros, Mario Zanon
Economic Model Predictive Control (MPC) dissipativity theory is central to discussing the stability of policies resulting from minimizing economic stage costs. In its current form,…