Fixed-Time Zeroing Neural Dynamics for Adaptive Coordination of Multi-Agent Systems

Abstract

This paper presents an adaptive multi-agent coordination (AMAC) strategy suitable for complex scenarios, which only requires information exchange between neighbouring robots. Unlike traditional multi-agent coordination methods that are solved by neural dynamics, the proposed strategy displays greater fiexibility, adaptability and scalability. Furthermore, the proposed AMAC strategy is reconstructed as a time-varying complex-valued matrix equation. By introducing a dynamic error function, a fixed-time convergent zeroing neural network (FTCZNN) model is designed for the online solution of the AMAC strategy, with its convergence time upper bound derived theoretically. Finally, the effectiveness and applicability of the coordination control method are demonstrated by numerical simulations and physical experiments. Numerical results indicate that this method can reduce the formation error to the order of 10 − 6 within 1.8 s.

Keywords

fixed-time convergence / multi-agent coordination / robotics / zeroing neural dynamics

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61962023, 61562029 and 62466019.

Conflicts of Interest

The authors declare no confiicts of interest.

Data Availability Statement

Data available on request from the authors.

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