Distributed Robust Semiglobal Consensus With Matched Uncertainties and Input Saturation
Lina Rong, Wenwen Zuo, Hui Gao, Shengyuan Xu · IEEE Transactions on Circuits & Systems II Express Briefs · 2021
This brief studies the robust semiglobal multi-agent consensus with linear subsystems subject to nonidentical parameter uncertainties and input saturation. A distributed low-gain-based nonlinear feedback law that avoids eigenvalues of the Laplacian matrix is established. It is proved that under the provided procedure, semiglobal multi-agent consensus is achieved when the lower bound of input saturation and the upper bound of parameter uncertainties satisfy certain relationship, which is shown as a design constraint in the provided method. The event-triggered scenario of the proposed protocol is further studied; the designs for the control law and the updating rule based on node-based event-driven mechanisms are provided and analyzed.