Distributed state estimator-based consensus tracking of multi-agent systems with exogenous disturbance

The consensus tacking problem for multi-agent systems with a leader of none control input and unknown control input is studied in this paper. By virtue of the relative state information of neighboring agents, state estimator and disturbance estimator are designed for each follower to estimate the system states and exogenous disturbance, respectively. Meanwhile, a novel control protocol based on two estimators is designed to make tracking error eventually converge to zero. Furthermore, the obtained results are further extended to the leader with unknown control input. A novel state estimator with adaptive time-varying gain is proposed such that consensus tracking condition is independent of the Laplacian matrix with regard to the communication topology. Finally, two examples are presented to verify the feasibility of the proposed control protocol. & COPY; 2022 Published by Elsevier Ltd on behalf of The Franklin Institute.

Automated summary

The paper investigates the consensus tacking problem for multi-agent systems with a leader of none control input and unknown control input. State estimator and disturbance estimator are designed for each follower based on the relative state information of neighboring agents to estimate the system states and exogenous disturbance, respectively. A novel control protocol based on two estimators is proposed to ensure the convergence of tracking error to zero. The results are further extended to the leader with unknown control input using a novel state estimator with adaptive time-varying gain, which eliminates the dependence on the Laplacian matrix of the communication topology. Two examples are presented to verify the feasibility of the proposed control protocol.