Quantized iterative learning control for nonlinear multi-agent systems with limited information communication and input saturation

This paper considers a quantized consensus problem for nonlinear multi-agent systems (MAS) using iterative learning control (ILC). For actual digital communication networks, agents can only transmit state information with limited bandwidth. Therefore, a Sigma-Delta (Sigma Delta) quantizer with a finite number of quantized bits is used to satisfy the communication network requirements. In addition, the introduction of network issues like input saturation and time delay make the problem more practically relevant. Due to the discontinuity caused by quantization, Filippov's non-smooth analysis theory is required to analyze the convergence performance of the MAS. The desired asymptotic consensus can be achieved with limited quantized information and possibly even a single bit between each pair of adjacent agents. Finally, numerical simulations are presented to illustrate the effectiveness of our theoretical analysis.