Bio-Inspired Self-Triggered Recurrent Neural Network for Solving Distributed Optimization Control Problem of Multi-Agent Systems
As multi-agent systems (MASs) expand in scale or dimensionality, ensuring efficient real-time distributed optimisation and consensus control becomes increasingly demanding, particularly when operating under constrained resources. To address this, the paper proposes and applies a distributed optimisation control scheme driven by bio-inspired self-triggered recurrent neural network (BSTRNN). Unlike conventional RNN that rely on periodic update schemes, the proposed BSTRNN adaptively selects update instants according to variations in internal states, thereby reducing redundant computation and improving overall efficiency. The bio-inspired architecture equips recurrent neural networks with improved stability, energy-efficient operation, and adaptive flexibility, allowing agents to converge rapidly to the global optimum while maintaining resilience against disturbances and uncertainties. Rigorous theoretical analysis identifies sufficient conditions that preclude the occurrence of Zeno behavior. Numerical simulations confirm the proposed approach, highlighting its ability to ensure consensus and achieve reliable tracking in resource-constrained MASs. Finally, the feasibility of the method is demonstrated through its deployment in a high-dimensional multi-robot arm system. Note to Practitioners—In this paper, a distributed neural dynamics optimization control method for multi-agent systems is discussed and applied to the consensus tracking task of multi-robot arms. The proposed method can enhance communication reliability during agents’ cooperative motion. This is mainly because, in multi-agent cooperative tasks, each agent operates under limited network bandwidth, and maintaining continuous communication over long periods not only causes network congestion but also results in high energy consumption. The existing approaches for collaborative motion generation in multi-agent systems are mostly developed based on the time-triggered mechanism. In this context, a challenging task arises, i.e., an on-demand updating method must be developed to ensure the seamless operation of the network. Meanwhile, the real-time performance of the cooperative task can be improved through a neural network-based solution strategy. Furthermore, the physical constraints of robot joints are considered to improve the general applicability of the proposed approach. The proposed BSTRNN scheme is applicable to distributed multi-agent systems with limited communication and computation resources, including drone swarms, networked robotic systems with bandwidth constraints, and cooperative multi-robot platforms.