Event-triggered model-free adaptive iterative learning scaled-consensus control for multi-agent systems under round-robin protocol

This paper proposes an event-triggered model-free adaptive iterative learning scaled-consensus control (ET-MFAILSCC) scheme to address scaled tracking in multi-agent systems under round-robin communication. First, dynamic linearization is employed to establish an equivalent data-driven model of the unknown nonlinear system in the iteration domain. Subsequently, a logarithmic quantizer is introduced to represent the quantization effects, and a model-free adaptive iterative learning scaled-consensus controller is developed using only quantized input/output (I/O) data. To mitigate network congestion under limited bandwidth, the round-robin protocol is adopted to schedule inter-agent data transmission. Furthermore, an event-triggered mechanism is designed to reduce unnecessary transmissions and communication consumption. Rigorous theoretical analysis proves the convergence of the scaled-consensus error along the iteration axis despite quantization errors and communication constraints. Finally, numerical simulations verify the effectiveness of the proposed method.