67 citations · 324 across the 46 of their papers we have counts for
30 papers · 1 filter
Asynchronous Model Predictive Control Under Model Mismatch: Stability and Performance Guarantees
Changrui Liu, Anil Alan, Shengling Shi +1
Certainty-equivalence model predictive control (CE-MPC) is widely used for its simplicity and efficiency, but theoretical guarantees under asynchronous feedback remain limited. Thi…
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,…
Current Practices in Electricy Demand and Charging Scheduling for On-Road Electric Fleet Operations: An Industry-Wide Review
Joost Commandeur, Bart De Schutter, Neil Yorke-Smith
The electrification of on-road fleet logistics promises improved air quality, lower noise emissions, major climate benefits, increased energy flexibility through the use of locally…
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…
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…
Predictive control barrier functions for piecewise affine systems with non-smooth constraints
Kanghui He, Anil Alan, Shengling Shi +2
Obtaining control barrier functions (CBFs) with large safe sets for complex nonlinear systems and constraints is a challenging task. Predictive CBFs address this issue by using an…
Approximate Model Predictive Control for Microgrid Energy Management via Imitation Learning
Changrui Liu, Shengling Shi, Anil Alan +2
Efficient energy management is essential for reliable and sustainable microgrid operation amid increasing renewable integration. In this paper, an imitation learning-based framewor…
Robust Adaptive Discrete-Time Control Barrier Certificate
Changrui Liu, Anil Alan, Shengling Shi +1
This work develops a robust adaptive control strategy for discrete-time systems using Control Barrier Functions (CBFs) to ensure safety under parametric model uncertainty and distu…
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…
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…
Sensitivity Analysis for Piecewise-Affine Approximations of Nonlinear Programs with Polytopic Constraints
Leila Gharavi, Changrui Liu, Bart De Schutter +1
Nonlinear Programs (NLPs) are prevalent in optimization-based control of nonlinear systems. Solving general NLPs is computationally expensive, necessitating the development of fast…
A Benchmark for the Application of Distributed Control Techniques to the Electricity Network of the European Economic Area
A. Riccardi, L. Laurenti, B. De Schutter
The European Economic Area Electricity Network Benchmark (EEA-ENB) is a multi-area power system representing the European network of transmission systems for electricity to facilit…
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…
State-action control barrier functions: Imposing safety on learning-based control with low online computational costs
Kanghui He, Shengling Shi, Ton van den Boom +1
Learning-based control with safety guarantees usually requires real-time safety certification and modifications of possibly unsafe learning-based policies. The control barrier func…
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…
A Behavioral Perspective on Models of Linear Dynamical Networks with Manifest Variables
Shengling Shi, Zhiyong Sun, Bart De Schutter
Networks of dynamical systems play an important role in various domains and have motivated many studies on the control and analysis of linear dynamical networks. For linear network…
Efficient MPC for Emergency Evasive Maneuvers, Part II: Comparative Assessment for Hybrid Control
Leila Gharavi, Bart De Schutter, Simone Baldi
Optimization-based approaches such as Model Predictive Control (MPC) are promising approaches in proactive control for safety-critical applications with changing environments such…
Efficient MPC for Emergency Evasive Maneuvers, Part I: Hybridization of the Nonlinear Problem
Leila Gharavi, Bart De Schutter, Simone Baldi
Despite the extensive application of nonlinear Model Predictive Control (MPC) in automated driving, balancing its computational efficiency with respect to the control performance a…
Approximate Dynamic Programming for Constrained Piecewise Affine Systems with Stability and Safety Guarantees
Kanghui He, Shengling Shi, Ton van den Boom +1
Infinite-horizon optimal control of constrained piecewise affine (PWA) systems has been approximately addressed by hybrid model predictive control (MPC), which, however, has comput…
Suboptimality analysis of receding horizon quadratic control with unknown linear systems and its applications in learning-based control
Shengling Shi, Anastasios Tsiamis, Bart De Schutter
This work analyzes how the trade-off between the modeling error, the terminal value function error, and the prediction horizon affects the performance of a nominal receding-horizon…
Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes
Filippo Airaldi, Bart De Schutter, Azita Dabiri
We propose a method to encourage safety in Model Predictive Control (MPC)-based Reinforcement Learning (RL) via Gaussian Process (GP) regression. This framework consists of 1) a pa…