1 citations · 2 across the 6 of their papers we have counts for
7 papers
Data-driven Kernel-based Predictive Control with Stability and Robustness Guarantees
Wenjie Liu, Yifei Li, Gang Wang +1
In this paper, we provide a theoretical analysis of the closed-loop properties of a data-driven kernel-based predictive control (DDKPC) scheme developed solely from input-output da…
Robust Data-Driven Nash Equilibrium Seeking under Partial-Decision Information
Linqi Wang, Yifei Li, Wenjie Liu +3
This paper presents a data-driven framework for decentralized Nash equilibrium (NE) seeking in multi-agent systems with unknown linear dynamics subject to exogenous disturbances, o…
Data-driven control of network systems: Accounting for communication adaptivity and security
Gang Wang, Wenjie Liu, Yifei Li +3
Over the past decades, network systems have surged in significance, driven by merging technological advancements. These systems play pivotal roles in diverse applications ranging f…
Data-driven Internal Model Control for Output Regulation
Wenjie Liu, Yifei Li, Jian Sun +4
Output regulation is a fundamental problem in control theory, extensively studied since the 1970s. Traditionally, research has primarily addressed scenarios where the system model…
Robust Control of Unknown Switched Linear Systems from Noisy Data
Wenjie Liu, Yifei Li, Jian Sun +2
This paper investigates the problem of data-driven stabilization for linear discrete-time switched systems with unknown switching dynamics. In the absence of noise, a data-based st…
Self-triggered Consensus Control of Multi-agent Systems from Data
Yifei Li, Xin Wang, Jian Sun +2
This paper considers self-triggered consensus control of unknown linear multi-agent systems (MASs). Self-triggering mechanisms (STMs) are widely used in MASs, thanks to their advan…
Data-driven Polytopic Output Synchronization of Heterogeneous Multi-agent Systems from Noisy Data
Yifei Li, Wenjie Liu, Jian Sun +3
This paper proposes a novel approach to addressing the output synchronization problem in unknown heterogeneous multi-agent systems (MASs) using noisy data. Unlike existing studies…