Search Results for author: Mario Zanon

Found 13 papers, 1 papers with code

A Semi-Distributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections

no code implementations19 Nov 2021 Robert Hult, Mario Zanon, Sebastien Gros, Paolo Falcone

In this paper, we consider the optimal coordination of automated vehicles at intersections under fixed crossing orders.

Distributed Optimization

Data-driven synthesis of Robust Invariant Sets and Controllers

no code implementations18 Nov 2021 Sampath Kumar Mulagaleti, Alberto Bemporad, Mario Zanon

This paper presents a method to identify an uncertain linear time-invariant (LTI) prediction model for tube-based Robust Model Predictive Control (RMPC).

Model Predictive Control with Infeasible Reference Trajectories

no code implementations10 Sep 2021 Ivo Batkovic, Mohammad Ali, Paolo Falcone, Mario Zanon

Model Predictive Control (MPC) formulations are typically built on the requirement that a feasible reference trajectory is available.

A New Dissipativity Condition for Asymptotic Stability of Discounted Economic MPC

no code implementations17 Jun 2021 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 nonlinear systems.

A Dissipativity Theory for Undiscounted Markov Decision Processes

no code implementations22 Apr 2021 Sébastien Gros, Mario Zanon

This generalization is based on nonlinear stage cost functionals, allowing one to discuss the Lyapunov asymptotic stability of policies for Markov Decision Processes in the set of probability measures.

Stability-Constrained Markov Decision Processes Using MPC

no code implementations2 Feb 2021 Mario Zanon, Sébastien Gros, Michele Palladino

This observation will entail that the MPC-based policy with stability requirements will produce the optimal policy for the discounted MDP if it is stable, and the best stabilizing policy otherwise.


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

2 code implementations29 Jan 2021 Nicolò Vallarano, Matteo Bruno, Emiliano Marchese, Giuseppe Trapani, Fabio Saracco, Tiziano Squartini, Giulio Cimini, Mario Zanon

Exponential Random Graph Models (ERGMs) have gained increasing popularity over the years.

Data Analysis, Statistics and Probability Statistical Mechanics

Safe Reinforcement Learning with Stability & Safety Guarantees Using Robust MPC

no code implementations14 Dec 2020 Sébastien Gros, Mario Zanon

Reinforcement Learning offers tools to optimize policies based on the data obtained from the real system subject to the policy.

reinforcement-learning Safe Reinforcement Learning

Reinforcement Learning for Mixed-Integer Problems Based on MPC

no code implementations3 Apr 2020 Sebastien Gros, Mario Zanon

Model Predictive Control has been recently proposed as policy approximation for Reinforcement Learning, offering a path towards safe and explainable Reinforcement Learning.

Q-Learning reinforcement-learning

Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?

no code implementations2 Apr 2020 Sebastien Gros, Mario Zanon, Alberto Bemporad

For all its successes, Reinforcement Learning (RL) still struggles to deliver formal guarantees on the closed-loop behavior of the learned policy.

Policy Gradient Methods Q-Learning +2

Constrained Controller and Observer Design by Inverse Optimality

no code implementations23 Mar 2020 Mario Zanon, Alberto Bemporad

When a baseline linear controller exists that is already well tuned in the absence of constraints and MPC is introduced to enforce them, one would like to avoid altering the original linear feedback law whenever they are not active.

Safe Trajectory Tracking in Uncertain Environments

no code implementations30 Jan 2020 Ivo Batkovic, Mohammad Ali, Paolo Falcone, Mario Zanon

In Model Predictive Control (MPC) formulations of trajectory tracking problems, infeasible reference trajectories and a-priori unknown constraints can lead to cumbersome designs, aggressive tracking, and loss of recursive feasibility.

A Computationally Efficient Model for Pedestrian Motion Prediction

no code implementations13 Mar 2018 Ivo Batkovic, Mario Zanon, Nils Lubbe, Paolo Falcone

We present a mathematical model to predict pedestrian motion over a finite horizon, intended for use in collision avoidance algorithms for autonomous driving.

Systems and Control

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