Lipo Mo

Prof. Lipo Mo


Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
25 March 2018 in International Journal of Robust and Nonlinear Control
Verified
Journal Article
Closed access
PDF file
LMLipo Mo
Lipo Mo
Peng LinPeng Lin
Peng Lin

20 December 2019 in IEEE Transactions on Neural Networks and Learning Systems
Verified
Journal Article
Closed access
YHYi Huang
Yi Huang
MDMengmeng Duan
Mengmeng Duan
LMLipo Mo
Lipo Mo
This brief studies the constrained containment control problem of continuous-time multiagent systems with nonconvex states constraints, nonuniform time delays, and switching directed networks. A distributed containment control algorithm consisting of nonlinear projection, nonlinear constraints, and communication delays is proposed. Based on the model transformation technique, convex analysis method, and the Lyapunov function, we prove that all agents finally converge into the convex hull formed by the given stationary points with arbitrarily bounded communication delays and arbitrarily switching networks as long as each agent jointly has at least one path from the given points to itself. Finally, a numerical example is included to show the effectiveness of theoretical results.

23 November 2018 in Journal of Systems Science and Complexity
Verified
Journal Article
Closed access
PDF file
LMLipo Mo
Lipo Mo
SGShaoyan Guo
Shaoyan Guo

1 April 2019 in Neurocomputing
Verified
Journal Article
Closed access
LMLipo Mo
Lipo Mo
XYXiaolin Yuan
Xiaolin Yuan
Yongguang YuYongguang Yu
Yongguang Yu

9 January 2020 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
Verified
Journal Article
Closed access
LMLipo Mo
Lipo Mo
Yongguang YuYongguang Yu
Yongguang Yu
Lin ZhaoLin Zhao
Lin Zhao
XCXianbing Cao
Xianbing Cao
This article discusses the distributed continuous-time optimization problem (DCTOP) of second-order multiagent systems (SOMASs). It is assumed that the inputs are required to be in some nonconvex sets, the team objective function (TOF) is a combination of general differentiable convex functions, and each agent can only obtain the information of one local objective function. Based on the neighbors' information, a new distributed continuous-time optimization algorithm (DCTOA) is first proposed for each agent, where its gradient gains are nonuniform. By introducing a scaling factor and a model transformation, the corresponding system is changed into a time-varying nonlinear system which does not contain constraint operator in form. Then, it is proven that all agents' states could reach an agreement and the TOF could be minimized by constructing some new Lyapunov functions. Finally, the effectiveness of the algorithm is shown by simulation results.

4 February 2020 in International Journal of Control, Automation, and Systems
Verified
Journal Article
Closed access
PDF file
XYXiao-Lin Yuan
Xiao-Lin Yuan
LMLipo Mo
Lipo Mo
YYYong-Guang Yu
Yong-Guang Yu
GRGuo-Jian Ren
Guo-Jian Ren