no code implementations • 17 Apr 2024 • Erkan Bayram, Shenyu Liu, Mohamed-Ali Belabbas, Tamer Başar
Given a training set in the form of a paired $(\mathcal{X},\mathcal{Y})$, we say that the control system $\dot{x} = f(x, u)$ has learned the paired set via the control $u^*$ if the system steers each point of $\mathcal{X}$ to its corresponding target in $\mathcal{Y}$.
no code implementations • 27 Jan 2024 • Shenyu Liu, Kaiwen Chen, Jaap Eising
This paper proposes a novel online data-driven adaptive control for unknown linear time-varying systems.
no code implementations • 14 Dec 2022 • Shenyu Liu, Antonio Russo
In this work we present further characterizations of integral input-to-state stability (iISS) for hybrid systems.
no code implementations • 3 Apr 2022 • Yuan Zhang, Yuanqing Xia, Shenyu Liu, Zhongqi Sun
In this paper, it is found that, if the input structure satisfies certain `regularizations', which are characterized by the proposed restricted total unimodulairty notion, those problems can be solvable in polynomial time via linear programming (LP) relaxations.
no code implementations • 14 Nov 2021 • Shenyu Liu
We then state our main result of the work, which requires the combined assessment of the total variation of the system matrix, the measure when the system is not sufficiently "stable" and the estimate of the perturbation to be upper bounded by a function affine in time.
no code implementations • 14 May 2021 • Shenyu Liu, Sonia Martinez, Jorge Cortes
This paper studies network resilience against structured additive perturbations to its topology.