14 citations · 46 across the 58 of their papers we have counts for
65 papers
Scen-Opt: A Scenario Optimization Toolbox for Data-Driven Convex Programming
Ben Wooding, Simone Garatti, Marco C. Campi +1
The scenario approach is a well-established statistical framework for data-driven decision-making. In particular, in data-driven optimization, the scenario approach unveils how the…
Data-Driven Formal Methods for Complex Dynamical Systems: A Survey
Behrad Samari, Alessandro Abate, Antoine Girard +3
Data-driven approaches with formal guarantees have recently emerged as a powerful means for the verification and controller synthesis of complex dynamical systems. Interest in thes…
Safe Packetized Control for Stochastic Constrained Networked Systems
Omid Akbarzadeh, MohammadHossein Ashoori, Mohammad H. Mamduhi +1
This work develops a formal framework for the synthesis of packetized safety controllers for discrete-time polynomial stochastic networked control systems (dt-PSNCS) operating unde…
Data-Driven Stabilizing Controller Design for Linear Infinite Networks
Mahdieh Zaker, Andrii Mironchenko, Amy Nejati +1
We propose a direct data-driven method for controller synthesis of infinite networks composed of unknown linear time-invariant subsystems. Using a single set of noise-corrupted inp…
k-Inductive Neural Barrier Certificates for Unknown Nonlinear Dynamics
Ben Wooding, Hongchao Zhang, Taylor T. Johnson +1
While conventional (k=1) discrete-time barrier certificate conditions impose strict safety constraints by requiring the function to be non-increasing at every step, k-inductive bar…
Data-Driven Global Stabilization of Unknown Infinite Networks
Mahdieh Zaker, Andrii Mironchenko, Amy Nejati +1
This paper develops a direct data-driven framework for infinite networks with unknown nonlinear polynomial subsystems, enabling the synthesis of controllers that ensure the entire…
Towards Certified Sim-to-Real Transfer via Stochastic Simulation-Gap Functions
P Sangeerth, Abolfazl Lavaei, Pushpak Jagtap
This paper introduces the notion of stochastic simulation-gap function, which formally quantifies the gap between an approximate mathematical model and a high-fidelity stochastic s…
From Noisy Data to Hierarchical Control: A Model-Order-Reduction Framework
Behrad Samari, Henrik Sandberg, Karl H. Johansson +1
This paper develops a direct data-driven framework for constructing reduced-order models (ROMs) of discrete-time linear dynamical systems with unknown dynamics and process disturba…
Safety Controller Synthesis for Stochastic Polynomial Time-Delayed Systems
Omid Akbarzadeh, MohammadHossein Ashoori, Amy Nejati +1
This work develops a theoretical framework for safety controller synthesis in discrete-time stochastic nonlinear polynomial systems subject to time-invariant delays (dt-SNPS-td). W…
A Data-Driven Krasovskii-Based Approach for Safety Controller Design of Time-Delayed Uncertain Polynomial Systems
Omid Akbarzadeh, MohammadHossein Ashoori, Amy Nejati +1
We develop a data-driven framework for the synthesis of robust Krasovskii control barrier certificates (RK-CBC) and corresponding robust safety controllers (R-SC) for discrete-time…
From Dissipativity Property to Data-Driven GAS Certificate of Degree-One Homogeneous Networks with Unknown Topology
Abolfazl Lavaei, David Angeli
In this work, we propose a data-driven divide and conquer strategy for the stability analysis of interconnected homogeneous nonlinear networks of degree one with unknown models and…
From Formal Methods to Data-Driven Safety Certificates of Unknown Large-Scale Networks
Omid Akbarzadeh, Behrad Samari, Amy Nejati +1
In this work, we propose a data-driven scheme within a compositional framework with noisy data to design robust safety controllers in a fully decentralized fashion for large-scale…
Data-Driven Adaptive Second-Order Sliding Mode Control with Noisy Data
Behrad Samari, Gian Paolo Incremona, Antonella Ferrara +1
This paper proposes a data-driven approach to designing adaptive suboptimal second-order sliding mode (ASSOSM) controllers for a class of single-input nonlinear systems with partia…
Data-Efficient Control of Polynomial Systems via Physics-Guided Quadratic Constraints
MohammadHossein Ashoori, Ali Aminzadeh, Amy Nejati +1
This work addresses the critical challenge of guaranteeing safety for complex dynamical systems where precise mathematical models are uncertain and data measurements are corrupted…
Data-Driven Stochastic Control: Foundations and Guarantees
Abolfazl Lavaei
This work establishes a step forward in advancing data-driven trajectory-based methods for stochastic systems with unknown mathematical dynamics. In contrast to scenario-based appr…
Data-Driven Incremental GAS Certificate of Nonlinear Homogeneous Networks: A Scenario Approach with Noisy Data
Mahdieh Zaker, David Angeli, Abolfazl Lavaei
This work focuses on a compositional data-driven approach to verify incremental global asymptotic stability (delta-GAS) over interconnected homogeneous networks of degree one with…
Data-Driven Model Order Reduction of Nonlinear Systems with Noisy Data
Behrad Samari, Henrik Sandberg, Karl H. Johansson +1
Model order reduction techniques simplify high-dimensional dynamical systems by deriving lower-dimensional models that retain essential system characteristics. These techniques are…
Safety Controller Synthesis for Stochastic Networked Systems under Communication Constraints
Omid Akbarzadeh, Mohammad H. Mamduhi, Abolfazl Lavaei
This paper develops a framework for synthesizing safety controllers for discrete-time stochastic linear control systems (dt-SLS) operating under communication imperfections. The co…
Data-Driven Safety Certificates of Infinite Networks with Unknown Models and Interconnection Topologies
Mahdieh Zaker, Amy Nejati, Abolfazl Lavaei
Infinite networks are complex interconnected systems comprising a countably infinite number of subsystems, for which no fixed upper bound on the number of participating subsystems…
ARCH-COMP25 Category Report: Stochastic Models
Alessandro Abate, Omid Akbarzadeh, Henk A. P. Blom +14
This report is concerned with a friendly competition for formal verification and policy synthesis of stochastic models. The main goal of the report is to introduce new benchmarks a…
TRUST: Stability and Safety Controller Synthesis for Unknown Dynamical Models Using a Single Trajectory
Jamie Gardner, Ben Wooding, Amy Nejati +1
TRUST is an open-source software tool developed for data-driven controller synthesis of dynamical systems with unknown mathematical models, ensuring either stability or safety prop…
From Data to Global Asymptotic Stability of Unknown Large-Scale Networks with Provable Guarantees
Mahdieh Zaker, Amy Nejati, Abolfazl Lavaei
We offer a compositional data-driven scheme for synthesizing controllers that ensure global asymptotic stability (GAS) across large-scale interconnected networks, characterized by…
Data-Driven Dynamic Controller Synthesis for Discrete-Time General Nonlinear Systems
Behrad Samari, Abolfazl Lavaei
Synthesizing safety controllers for general nonlinear systems is a highly challenging task, particularly when the system models are unknown, and input constraints are present. Whil…
From Data to Sliding Mode Control of Uncertain Large-Scale Networks with Unknown Dynamics
Behrad Samari, Gian Paolo Incremona, Antonella Ferrara +1
In this paper, we develop a compositional data-driven approach for the global stabilization of large-scale nonlinear networks with unknown dynamics and external perturbations. We f…