341 citations · 769 across the 30 of their papers we have counts for
34 papers
Optimality Conditions for Model Predictive Control: Rethinking Predictive Model Design
Akhil S Anand, Arash Bahari Kordabad, Mario Zanon +1
Optimality is a critical aspect of Model Predictive Control (MPC), especially in economic MPC. However, achieving optimality in MPC presents significant challenges, and may even be…
Computation of safe disturbance sets using implicit RPI sets
Sampath Kumar Mulagaleti, Alberto Bemporad, Mario Zanon
Given a stable linear time-invariant (LTI) system subject to output constraints, we present a method to compute a set of disturbances such that the reachable set of outputs matches…
Experimental Validation of Safe MPC for Autonomous Driving in Uncertain Environments
Ivo Batkovic, Ankit Gupta, Mario Zanon +1
The full deployment of autonomous driving systems on a worldwide scale requires that the self-driving vehicle be operated in a provably safe manner, i.e., the vehicle must be able…
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…
Performance Quantification of a Nonlinear Model Predictive Controller by Parallel Monte Carlo Simulations of a Closed-loop System
Morten Wahlgreen Kaysfeld, Mario Zanon, John Bagterp Jørgensen
This paper presents a parallel Monte Carlo simulation based performance quantification method for nonlinear model predictive control (NMPC) in closed-loop. The method provides dist…
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…
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…
Data-driven synthesis of Robust Invariant Sets and Controllers
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). The uncertain model is det…
Computation of Input Disturbance Sets for Constrained Output Reachability
Sampath Kumar Mulagaleti, Alberto Bemporad, Mario Zanon
Linear models with additive unknown-but-bounded input disturbances are extensively used to model uncertainty in robust control systems design. Typically, the disturbance set is eit…
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…
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…
Model Predictive Control with Environment Adaptation for Legged Locomotion
Niraj Rathod, Angelo Bratta, Michele Focchi +4
Re-planning in legged locomotion is crucial to track the desired user velocity while adapting to the terrain and rejecting external disturbances. In this work, we propose and test…
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,…
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…
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…
Learning for MPC with Stability & Safety Guarantees
Sébastien Gros, Mario Zanon
The combination of learning methods with Model Predictive Control (MPC) has attracted a significant amount of attention in the recent literature. The hope of this combination is to…
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…
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…
Robust Control Invariance and Communication Scheduling in Lossy Wireless Networked Control Systems
Masoud Bahraini, Mario Zanon, Paolo Falcone +1
In Networked Control Systems (NCS) impairments of the communication channel can be disruptive to stability and performance. In this paper, we consider the problem of scheduling the…
Reinforcement Learning Based on Real-Time Iteration NMPC
Mario Zanon, Vyacheslav Kungurtsev, Sébastien Gros
Reinforcement Learning (RL) has proven a stunning ability to learn optimal policies from data without any prior knowledge on the process. The main drawback of RL is that it is typi…
Reinforcement Learning for Mixed-Integer Problems Based on MPC
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. This ap…
Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?
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. Among other things, guaranteeing…
Constrained Controller and Observer Design by Inverse Optimality
Mario Zanon, Alberto Bemporad
Model Predictive Control (MPC) is often tuned by trial and error. When a baseline linear controller exists that is already well tuned in the absence of constraints and MPC is intro…
Safe Trajectory Tracking in Uncertain Environments
Ivo Batkovic, Mohammad Ali, Paolo Falcone +1
In Model Predictive Control (MPC) formulations of trajectory tracking problems, infeasible reference trajectories and a-priori unknown constraints can lead to cumbersome designs, a…