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20232026
most citedSTORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning

8 citations · 13 across the 16 of their papers we have counts for

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10 papers · 1 filter

eess.SY2026

Data-Driven Spiking Control for Distributed -Nash Equilibrium Seeking

Linqi Wang, Wei Xiao, Yuzhou Wei +3

This paper studies how a feedback law synthesized directly from data can be realized by spiking control while retaining a game-theoretic performance guarantee. We consider distribu…

eess.SY2026

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…

eess.SY2026

Data-Driven Robust MPC for Unknown Nonlinear Systems via Set-Membership Learning

Yuzhou Wei, Wenjie Liu, Yifan Xie +3

Data-driven model predictive control (MPC) has become an attractive approach for controlling unknown systems, especially when data are corrupted by noise. However, most existing da…

eess.SY2026

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…

eess.SY2025

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…

eess.SY2025

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…

eess.SY2024

Distributed Data-driven Unknown-input Observers for State Estimation

Yuzhou Wei, Giorgia Disarò, Wenjie Liu +3

Unknown inputs related to, e.g., sensor aging, modeling errors, or device bias, represent a major concern in wireless sensor networks, as they degrade the state estimation performa…

eess.SY2023★ 1 cited

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…

eess.SY2023★ 3 cited

Data-driven Control Against False Data Injection Attacks

Wenjie Liu, Lidong Li, Jian Sun +3

The rise of cyber-security concerns has brought significant attention to the analysis and design of cyber-physical systems (CPSs). Among the various types of cyberattacks, denial-o…

eess.SY2023★ 1 cited

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…