A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 8 Issue 9
Sep.  2021

IEEE/CAA Journal of Automatica Sinica

• JCR Impact Factor: 7.847, Top 10% (SCI Q1)
CiteScore: 13.0, Top 5% (Q1)
Google Scholar h5-index: 64， TOP 7
Turn off MathJax
Article Contents
Mohammad Saeed Sarafraz and Mohammad Saleh Tavazoei, "A Unified Optimization-Based Framework to Adjust Consensus Convergence Rate and Optimize the Network Topology in Uncertain Multi-Agent Systems," IEEE/CAA J. Autom. Sinica, vol. 8, no. 9, pp. 1539-1548, Sept. 2021. doi: 10.1109/JAS.2021.1004111
 Citation: Mohammad Saeed Sarafraz and Mohammad Saleh Tavazoei, "A Unified Optimization-Based Framework to Adjust Consensus Convergence Rate and Optimize the Network Topology in Uncertain Multi-Agent Systems," IEEE/CAA J. Autom. Sinica, vol. 8, no. 9, pp. 1539-1548, Sept. 2021.

# A Unified Optimization-Based Framework to Adjust Consensus Convergence Rate and Optimize the Network Topology in Uncertain Multi-Agent Systems

##### doi: 10.1109/JAS.2021.1004111
• This paper deals with the consensus problem in an uncertain multi-agent system whose agents communicate with each other through a weighted undirected (primary) graph. The considered multi-agent system is described by an uncertain state-space model in which the involved matrices belong to some matrix boxes. As the main contribution of the paper, a unified optimization-based framework is proposed for simultaneously reducing the weights of the edges of the primary communication graph (optimizing the network topology) and synthesizing a controller such that the consensus in the considered uncertain multi-agent system is ensured with an adjustable convergence rate. Considering the NP-hardness nature of the optimization problem related to the aforementioned framework, this problem is relaxed such that it can be solved by regular LMI solvers. Numerical/practical-based examples are presented to verify the usefulness of the obtained results.

• 1 Formally speaking, there is a bilinear term in the constraint of the optimization (18) which can cause non-convexity. However, since the only source of non-convexity is the scalar variable $\beta$, a straightforward approach is to adjust this variable through a grid-search or some other efficient methods like as that proposed in [52].
2 http://www.complib.de/

### Catalog

###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

Figures(4)