In this paper, the cluster synchronization problem of a heterogeneous second-order leader-following multi-agent system with nonlinear dynamics, actuator faults, and integral quadratic constraints (IQCs) under a directed topology with a directed spanning tree is investigated. Based on the local topology information, two adaptive fault-tolerant pinning control strategies with fixed and adaptive pinning gains are proposed to guarantee cluster synchronization in finite time. An adaptive input compensation is developed to attenuate the adverse effects of actuator faults. It is worth mentioning that just one parameter needs to be estimated for each agent in this compensation, which implies that the strategies designed in this paper can effectively reduce the computational cost. Furthermore, the use of the pinning control method instead of the fully equipped control method makes the strategies more cost-effective for large-scale multi-agent systems. Finally, numerical simulation examples are introduced to demonstrate the effectiveness and advantages of the proposed strategies.
Bibliographical noteFunding Information:
This work was supported by National Natural Science Foundation of China (Grant No. 61773056), Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB (Grant No. BK19AE018), and Fundamental Research Funds for the Central Universities of USTB (FRF-TP-20-09B, 230201606500061, FRF-DF-20-35, FRF-BD-19-002A), and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449. The work of J.L. Wang was supported by Zhejiang Natural Science Foundation (Grant No. LD21F030001).
© 2021 Elsevier Inc.
- Cluster synchronization
- Fault-tolerant control
- Integral quadratic constraints (IQCs)
- Multi-agent system
- Pinning control
ASJC Scopus subject areas
- Theoretical Computer Science
- Control and Systems Engineering
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence