Robust consensus control of nonlinear multi-agent systems based on convergence rate estimation

Convergence rate is an important performance criterion for consensus control of multi-agent systems (MASs). It has been an important and difficult problem to accelerate the convergence rate of MASs. In this article, a robust consensus control scheme based on the convergence rate estimation for a nonlinear high-order MAS is proposed to accelerate the convergence rate as well as improve the control performance of the MAS. In the proposed control scheme, a novel convergence rate indicator (CRI) is defined to measure the influence of the model nonlinearity, the state couplings within a single agent and among multiple agents in the MAS on convergence rate. Then, a distributed consensus controller with a CRI-based additive term is designed by the backstepping technique with disturbance observers to enhance the robustness of the closed-loop system. Furthermore, from a theoretical perspective, the robustness and the stability of the MAS are proved. Finally, the consensus control problem of the multi-agent control system composed of five single-link robot arms is simulated to validate the effectiveness of the proposed controller. Simulation results illustrate that the proposed consensus control approach with CRI has better performance than conventional backstepping consensus control techniques of the MAS in the same conditions.