5 papers
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 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…
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…
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…
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…