activity
20202022
most citedConsensus on Matrix-weighted Time-varying Networks

4 citations · 10 across the 8 of their papers we have counts for

collaborators

9 papers

eess.SY2022

Vector-valued Privacy-Preserving Average Consensus

Lulu Pan, Haibin Shao, Yang Lu +3

Achieving average consensus without disclosing sensitive information can be a critical concern for multi-agent coordination. This paper examines privacy-preserving average consensu…

eess.SY2022

A co-design method of online learning SMC law via an input-mappping strategy

Yaru Yu, Dewei Li, Dongya Zhao +1

The research on sliding mode control strategy is generally based on the robust approach. The larger parameter space consideration will inevitably sacrifice part of the performance.…

eess.SY20211 cited

Event-triggered Consensus of Matrix-weighted Networks Subject to Actuator Saturation

Lulu Pan, Haibin Shao, Yuanlong Li +2

The ubiquitous interdependencies among higher-dimensional states of neighboring agents can be characterized by matrix-weighted networks. This paper examines event-triggered global…

eess.SY20212 cited

Cluster Consensus on Matrix-weighted Switching Networks

Lulu Pan, Haibin Shao, Mehran Mesbahi +2

This paper examines the cluster consensus problem of multi-agent systems on matrix-weighted switching networks. Necessary and/or sufficient conditions under which cluster consensus…

math.OC20203 cited

Data-driven approximation for feasible regions in nonlinear model predictive control

Yuanqiang Zhou, Dewei Li, Yugeng Xi +1

This paper develops a data-driven learning framework for approximating the feasible region and invariant set of a nonlinear system under the nonlinear Model Predictive Control (MPC…

math.OC2020

Data-Driven Predictive Control for Continuous-Time Industrial Processes with Completely Unknown Dynamics

Yuanqiang Zhou, Dewei Li, Yugeng Xi

This paper investigates the data-driven predictive control problems for a class of continuous-time industrial processes with completely unknown dynamics. The proposed approach empl…