activity
20182022
most citedA Robust Distributed Model Predictive Control Framework for Consensus of Multi-Agent Systems with Input Constraints and Varying Delays

2 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

math.OC2022

Rate analysis of dual averaging for nonconvex distributed optimization

Changxin Liu, Xuyang Wu, Xinlei Yi +2

This work studies nonconvex distributed constrained optimization over stochastic communication networks. We revisit the distributed dual averaging algorithm, which is known to conv…

eess.SY20222 cited

A Robust Distributed Model Predictive Control Framework for Consensus of Multi-Agent Systems with Input Constraints and Varying Delays

Henglai Wei, Changxin Liu, Yang Shi

This paper studies the consensus problem of general linear discrete-time multi-agent systems (MAS) with input constraints and bounded time-varying communication delays. We propose…

math.OC2019

Self-Triggered Adaptive Model Predictive Control of Constrained Nonlinear Systems: A Min-Max Approach

Kunwu Zhang, Changxin Liu, Yang Shi

In this paper, a self-triggered adaptive model predictive control (MPC) algorithm is proposed for constrained discrete-time nonlinear systems subject to parametric uncertainties an…

math.OC2019

Towards an convergence rate for distributed dual averaging

Changxin Liu, Huiping Li, Yang Shi

Recently, distributed dual averaging has received increasing attention due to its superiority in handling constraints and dynamic networks in multiagent optimization. However, all…

math.OC2019

Resource-aware Exact Decentralized Optimization Using Event-triggered Broadcasting

Changxin Liu, Huiping Li, Yang Shi

This work addresses the decentralized optimization problem where a group of agents with coupled private objective functions work together to exactly optimize the summation of local…

math.OC2018

A unitary distributed subgradient method for multi-agent optimization with different coupling sources

Changxin Liu, Huiping Li, Yang Shi

In this work, we first consider distributed convex constrained optimization problems where the objective function is encoded by multiple local and possibly nonsmooth objectives pri…