28 citations · 77 across the 23 of their papers we have counts for
25 papers
Composite-Gradient Learning for Shared Control Authority Between Deep Reinforcement Learning and Model Predictive Control
Giray Önür, Azita Dabiri, Bart De Schutter
Integrated deep reinforcement learning (DRL) and model predictive control (MPC) methods are increasingly used to control autonomous systems by combining their complementary capabil…
Stochastic Nonlinear Model Predictive Control with Gaussian Mixture Uncertainty Propagation
Konstantinos Prattis, Luca Laurenti, Azita Dabiri
We propose a novel Stochastic Nonlinear Model Predictive Control (SNMPC) framework for nonlinear systems with additive noise. Building on recent advances in nonlinear uncertainty p…
Sharing the Control Authority Between Deep Reinforcement Learning and Model Predictive Control: Application to Multi-Class Transportation Networks
Giray Onur, Azita Dabiri, Bart De Schutter
Transportation networks, in particular multi-class transportation networks (i.e., networks with mixed vehicle types), are complex systems that are challenging to control. Recently,…
Integrated Online Monitoring and Adaptation of Process Model Predictive Controllers
Samuel Mallick, Laura Boca de de Giuli, Alessio La Bella +3
This paper addresses the design of an event-triggered, data-based, and performance-oriented adaption method for model predictive control (MPC). The performance of such a strategy s…
Reinforcement Learning with Distributed MPC for Fuel-Efficient Platoon Control with Discrete Gear Transitions
Samuel Mallick, Gianpietro Battocletti, Dimitris Boskos +2
Cooperative control of groups of autonomous vehicles (AVs), i.e., platoons, is a promising direction to improving the efficiency of autonomous transportation systems. In this conte…
Safe model-based Reinforcement Learning via Model Predictive Control and Control Barrier Functions
Kerim Dzhumageldyev, Filippo Airaldi, Azita Dabiri
Optimal control strategies are often combined with safety certificates to ensure both performance and safety in safety-critical systems. A prominent example is combining Model Pred…
Adaptive Tuning of Parameterized Traffic Controllers via Multi-Agent Reinforcement Learning
Giray Önür, Azita Dabiri, Bart De Schutter
Effective traffic control is essential for mitigating congestion in transportation networks. Conventional traffic management strategies, including route guidance and ramp metering,…
Model Predictive Control and Moving Horizon Estimation using Statistically Weighted Data-Based Ensemble Models
Laura Boca de Giuli, Samuel Mallick, Alessio La Bella +3
This paper presents a model predictive control (MPC) framework leveraging an ensemble of data-based models to optimally control complex systems under multiple operating conditions.…
Second-Order MPC-Based Distributed Q-Learning
Samuel Mallick, Filippo Airaldi, Azita Dabiri +1
The state of the art for model predictive control (MPC)-based distributed Q-learning is limited to first-order gradient updates of the MPC parameterization. In general, using secon…
A state reduction approach for learning-based model predictive control for train rescheduling
Caio Fabio Oliveira da Silva, Xiaoyu Liu, Azita Dabiri +1
This paper proposes a state reduction method for learning-based model predictive control (MPC) for train rescheduling in urban rail transit systems. The state reduction integrates…
Distributed model predictive control without terminal cost under inexact distributed optimization
Xiaoyu Liu, Dimos V. Dimarogonas, Changxin Liu +2
This paper presents a novel distributed model predictive control (MPC) formulation without terminal cost and a corresponding distributed synthesis approach for distributed linear d…
Probabilistically safe and efficient model-based reinforcement learning
Filippo Airaldi, Bart De Schutter, Azita Dabiri
This paper proposes tackling safety-critical stochastic Reinforcement Learning (RL) tasks with a sample-based, model-based approach. At the core of the method lies a Model Predicti…
Learning-Based MPC for Fuel Efficient Control of Autonomous Vehicles with Discrete Gear Selection
Samuel Mallick, Gianpietro Battocletti, Qizhang Dong +2
Co-optimization of both vehicle speed and gear position via model predictive control (MPC) has been shown to offer benefits for fuel-efficient autonomous driving. However, optimizi…
Optimization-based Coordination of Traffic Lights and Automated Vehicles at Intersections
Azita Dabiri, Giray Önür, Sebastien Gros +1
This paper tackles the challenge of coordinating traffic lights and automated vehicles at signalized intersections, formulated as a constrained finite-horizon optimal control probl…
Learning-based model predictive control for passenger-oriented train rescheduling with flexible train composition
Xiaoyu Liu, Caio Fabio Oliveira da Silva, Azita Dabiri +2
This paper focuses on passenger-oriented real-time train rescheduling, considering flexible train composition and rolling stock circulation, by integrating learning-based and optim…
Nonmyopic Global Optimisation via Approximate Dynamic Programming
Filippo Airaldi, Bart De Schutter, Azita Dabiri
Global optimisation to optimise expensive-to-evaluate black-box functions without gradient information. Bayesian optimisation, one of the most well-known techniques, typically empl…
Learning-Based Model Predictive Control for Piecewise Affine Systems with Feasibility Guarantees
Samuel Mallick, Azita Dabiri, Bart De Schutter
Online model predictive control (MPC) for piecewise affine (PWA) systems requires the online solution to an optimization problem that implicitly optimizes over the switching sequen…
Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control
Samuel Mallick, Filippo Airaldi, Azita Dabiri +2
Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which…
Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids
Caio Fabio Oliveira da Silva, Azita Dabiri, Bart De Schutter
This work proposes an approach that integrates reinforcement learning and model predictive control (MPC) to solve finite-horizon optimal control problems in mixed-logical dynamical…
Distributed Model Predictive Control for Piecewise Affine Systems Based on Switching ADMM
Samuel Mallick, Azita Dabiri, Bart De Schutter
This paper presents a novel approach for distributed model predictive control (MPC) for piecewise affine (PWA) systems. Existing approaches rely on solving mixed-integer optimizati…
A Comparison Benchmark for Distributed Hybrid MPC Control Methods: Distributed Vehicle Platooning
Samuel Mallick, Azita Dabiri, Bart De Schutter
Distributed model predictive control (MPC) is currently being investigated as a solution to the important control challenge presented by networks of hybrid dynamical systems. Howev…
Multi-Agent Reinforcement Learning via Distributed MPC as a Function Approximator
Samuel Mallick, Filippo Airaldi, Azita Dabiri +1
This paper presents a novel approach to multi-agent reinforcement learning (RL) for linear systems with convex polytopic constraints. Existing work on RL has demonstrated the use o…
Reinforcement Learning with Model Predictive Control for Highway Ramp Metering
Filippo Airaldi, Bart De Schutter, Azita Dabiri
In the backdrop of an increasingly pressing need for effective urban and highway transportation systems, this work explores the synergy between model-based and learning-based strat…
Proactive Emergency Collision Avoidance for Automated Driving in Highway Scenarios
Leila Gharavi, Azita Dabiri, Jelske Verkuijlen +2
Uncertainty in the behavior of other traffic participants is a crucial factor in collision avoidance for automated driving; here, stochastic metrics could avoid overly conservative…