341 citations · 769 across the 30 of their papers we have counts for
24 papers · 1 filter
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…
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…
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…
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,…
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…
Towards Safe Reinforcement Learning Using NMPC and Policy Gradients: Part I - Stochastic case
Sebastien Gros, Mario Zanon
We present a methodology to deploy the stochastic policy gradient method, using actor-critic techniques, when the optimal policy is approximated using a parametric optimization pro…
Towards Safe Reinforcement Learning Using NMPC and Policy Gradients: Part II - Deterministic Case
Sebastien Gros, Mario Zanon
In this paper, we present a methodology to deploy the deterministic policy gradient method, using actor-critic techniques, when the optimal policy is approximated using a parametri…
Safe Reinforcement Learning Using Robust MPC
Mario Zanon, Sébastien Gros
Reinforcement Learning (RL) has recently impressed the world with stunning results in various applications. While the potential of RL is now well-established, many critical aspects…
Practical Reinforcement Learning of Stabilizing Economic MPC
Mario Zanon, Sébastien Gros, Alberto Bemporad
Reinforcement Learning (RL) has demonstrated a huge potential in learning optimal policies without any prior knowledge of the process to be controlled. Model Predictive Control (MP…
Data-driven Economic NMPC using Reinforcement Learning
Sébastien Gros, Mario Zanon
Reinforcement Learning (RL) is a powerful tool to perform data-driven optimal control without relying on a model of the system. However, RL struggles to provide hard guarantees on…
Real-Time Constrained Trajectory Planning and Vehicle Control for Proactive Autonomous Driving With Road Users
Ivo Batkovic, Mario Zanon, Mohammad Ali +1
For motion planning and control of autonomous vehicles to be proactive and safe, pedestrians' and other road users' motions must be considered. In this paper, we present a vehicle…
A Computationally Efficient Model for Pedestrian Motion Prediction
Ivo Batkovic, Mario Zanon, Nils Lubbe +1
We present a mathematical model to predict pedestrian motion over a finite horizon, intended for use in collision avoidance algorithms for autonomous driving. The model is based on…