1 citations · 1 across the 11 of their papers we have counts for
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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-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…
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…
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…
Model Order Reduction from Data with Certification
Behrad Samari, Amy Nejati, Abolfazl Lavaei
Model order reduction (MOR) involves offering low-dimensional models that effectively approximate the behavior of complex high-order systems. Due to potential model complexities an…