Decentralized Robust Adaptive Control for the Multiagent System Consensus Problem Using Neural Networks

TLDR

A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with previous work, the agents’ dynamics include uncertainties and external disturbances, making the approach more practical for real‑world applications. The method employs adaptive neural networks to compensate uncertain dynamics, a robustness signal to counter approximation error and disturbances, and a decentralized controller that uses only neighboring agent information, and it is extended to prescribed formations and higher‑order dynamics. Theoretical analysis shows the consensus error can be made arbitrarily small, and simulations confirm satisfactory performance.

Abstract

A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with the previous work, the agent's dynamics includes the uncertainties and external disturbances, which is more practical in real-world applications. Due to the approximation capability of neural networks, the uncertain dynamics is compensated by the adaptive neural network scheme. The effects of the approximation error and external disturbances are counteracted by employing the robustness signal. The proposed algorithm is decentralized because the controller for each agent only utilizes the information of its neighbor agents. By the theoretical analysis, it is proved that the consensus error can be reduced as small as desired. The proposed method is then extended to two cases: agents form a prescribed formation, and agents have the higher order dynamics. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed method.