8 papers · 1 filter
Temporal Logic Control of Nonlinear Stochastic Systems with Online Performance Optimization
Alessandro Riccardi, Thom Badings, Luca Laurenti +2
The deployment of autonomous systems in safety-critical environments requires control policies that guarantee satisfaction of complex control specifications. These systems are comm…
Dodging the Moose: Experimental Insights in Real-Life Automated Collision Avoidance
Leila Gharavi, Simone Baldi, Yuki Hosomi +4
The sudden appearance of a static obstacle on the road, i.e. the moose test, is a well-known emergency scenario in collision avoidance for automated driving. Model Predictive Contr…
Dynamics of Implicit Time-Invariant Max-Min-Plus-Scaling Discrete-Event Systems
Sreeshma Markkassery, Ton van den Boom, Bart De Schutter
Max-min-plus-scaling (MMPS) systems generalize max-plus, min-plus and max-min-plus models with more flexibility in modelling discrete-event dynamics. Especially, implicit MMPS mode…
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…
Partitioning techniques for non-centralized predictive control: A systematic review and novel theoretical insights
Alessandro Riccardi, Luca Laurenti, Bart De Schutter
The partitioning problem is of central relevance for designing and implementing non-centralized Model Predictive Control (MPC) strategies for large-scale systems. These control app…
Learning State-Action Control Barrier Functions for Model-Free Control under Constraints
Kanghui He, Shengling Shi, Ton van den Boom +1
Ensuring constraint satisfaction for learning-based control is a critical challenge, especially in the model-free case. While safety filters address this challenge in the model-bas…
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…
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…