22 citations · 106 across the 18 of their papers we have counts for
19 papers
Memory-Dependent Interval Markov Chain Abstractions of Stochastic Dynamics
Menno van Zutphen, Adrien Banse, Domagoj Herceg +2
Finite-state interval Markov-chain (IMC) abstractions provide sound verification and performance bounds for continuous-state stochastic systems by enclosing cell-to-cell transition…
Formal Entropy-Regularized Control of Stochastic Systems
Menno van Zutphen, Giannis Delimpaltadakis, Duarte J. Antunes
Analyzing and controlling system entropy is a powerful tool for regulating predictability of control systems. Applications benefiting from such approaches range from reinforcement…
Safe Feedback Optimization through Control Barrier Functions
Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés +1
Feedback optimization refers to a class of methods that steer a control system to a steady state that solves an optimization problem. Despite tremendous progress on the topic, an i…
Tempering the Bayes Filter towards Improved Model-Based Estimation
Menno van Zutphen, Domagoj Herceg, Giannis Delimpaltadakis +1
Model-based filtering is often carried out while subject to an imperfect model, as learning partially-observable stochastic systems remains a challenge. Recent work on Bayesian inf…
An Information Theory of Finite Abstractions and their Fundamental Scalability Limits
Giannis Delimpaltadakis, Gabriel Gleizer
Finite abstractions are discrete models of dynamical systems, such that the set of abstraction trajectories contains all system trajectories. There is a consensus that abstractions…
Feedback Optimization with State Constraints through Control Barrier Functions
Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés +1
Recently, there has been a surge of research on a class of methods called feedback optimization. These are methods to steer the state of a control system to an equilibrium that ari…
Memory-dependent abstractions of stochastic systems through the lens of transfer operators
Adrien Banse, Giannis Delimpaltadakis, Luca Laurenti +2
With the increasing ubiquity of safety-critical autonomous systems operating in uncertain environments, there is a need for mathematical methods for formal verification of stochast…
Predictable Interval MDPs through Entropy Regularization
Menno van Zutphen, Giannis Delimpaltadakis, Maurice Heemels +1
Regularization of control policies using entropy can be instrumental in adjusting predictability of real-world systems. Applications benefiting from such approaches range from, e.g…
Continuous Approximations of Projected Dynamical Systems via Control Barrier Functions
Giannis Delimpaltadakis, Jorge Cortés, W. P. M. H. Heemels
Projected Dynamical Systems (PDSs) form a class of discontinuous constrained dynamical systems, and have been used widely to solve optimization problems and variational inequalitie…
Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization
Daniel Jarne Ornia, Giannis Delimpaltadakis, Jens Kober +1
In Reinforcement Learning (RL), agents have no incentive to exhibit predictable behaviors, and are often pushed (through e.g. policy entropy regularisation) to randomise their acti…
On the relationship between control barrier functions and projected dynamical systems
Giannis Delimpaltadakis, W. P. M. H. Heemels
In this paper, we study the relationship between systems controlled via Control Barrier Function (CBF) approaches and a class of discontinuous dynamical systems, called Projected D…
Interval Markov Decision Processes with Continuous Action-Spaces
Giannis Delimpaltadakis, Morteza Lahijanian, Manuel Mazo +1
Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of res…
ETCetera: beyond Event-Triggered Control
Giannis Delimpaltadakis, Gabriel de A. Gleizer, Ivo van Straalen +1
We present ETCetera, a Python library developed for the analysis and synthesis of the sampling behaviour of event triggered control (ETC) systems. In particular, the tool construct…
Formal Analysis of the Sampling Behaviour of Stochastic Event-Triggered Control
Giannis Delimpaltadakis, Luca Laurenti, Manuel Mazo
Analyzing Event-Triggered Control's (ETC) sampling behaviour is of paramount importance, as it enables formal assessment of its sampling performance and prediction of its sampling…
Abstracting the Sampling Behaviour of Stochastic Linear Periodic Event-Triggered Control Systems
Giannis Delimpaltadakis, Luca Laurenti, Manuel Mazo
Recently, there have been efforts towards understanding the sampling behaviour of event-triggered control (ETC), for obtaining metrics on its sampling performance and predicting it…
Abstracting the Traffic of Nonlinear Event-Triggered Control Systems
Giannis Delimpaltadakis, Manuel Mazo
Scheduling communication traffic in networks of event-triggered control (ETC) systems is challenging, as their sampling times are unknown, hindering application of ETC in networks.…
Region-Based Self-Triggered Control for Perturbed and Uncertain Nonlinear Systems
Giannis Delimpaltadakis, Manuel Mazo
In this work, we derive a region-based self-triggered control (STC) scheme for nonlinear systems with bounded disturbances and model uncertainties. The proposed STC scheme is able…
Traffic Abstractions of Nonlinear Homogeneous Event-Triggered Control Systems
Giannis Delimpaltadakis, Manuel Mazo
In previous work, linear time-invariant event-triggered control (ETC) systems were abstracted to finite-state systems that capture the original systems' sampling behaviour. It was…
Isochronous Partitions for Region-Based Self-Triggered Control
Giannis Delimpaltadakis, Manuel Mazo
In this work, we propose a region-based self-triggered control (STC) scheme for nonlinear systems. The state space is partitioned into a finite number of regions, each of which is…