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20242026
most citedCertified Learning of Incremental ISS Controllers for Unknown Nonlinear Polynomial Dynamics

2 citations · 2 across the 5 of their papers we have counts for

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5 papers

eess.SY2026

Data-Driven Synthesis of Robust Positively Invariant Sets from Noisy Data

Chi Wang, David Angeli

This paper develops a method to construct robust positively invariant (RPI) tube sets from finite noisy input-state data of an unknown linear time-invariant (LTI) system, yielding…

eess.SY2026

Tube-Based Robust Data-Driven Predictive Control

Chi Wang, David Angeli

This paper presents a tractable tube-based robust data-driven predictive control scheme that uses only a single finite noisy input-state trajectory of an unknown discrete-time line…

eess.SY2025

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…

eess.SY2025

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…

eess.SY2024★ 2 cited

Certified Learning of Incremental ISS Controllers for Unknown Nonlinear Polynomial Dynamics

Mahdieh Zaker, David Angeli, Abolfazl Lavaei

Incremental input-to-state stability (delta-ISS) offers a robust framework to ensure that small input variations result in proportionally minor deviations in the state of a nonline…