Iterative Learning Control for Multi-Agent Systems with Finite-Leveled Sigma-Delta Quantizer and Input Saturation
Ting Zhang, Junmin Li · 2021
This paper investigates the consensus problem of leader-following multi-agent systems (MAS) by using the iterative learning control approach with quantization and input saturation, and gives a visible distributed quantized protocol to update the dynamic systems with Sigma-Delta (ΣΔ) quantizer which has a finite number of quantization level. Since quantization will introduce nonlinearities and uncertainties into systems, a robust compensation learning control scheme is utilized to overcome the difficulty, and the asymptotical convergence analysis can be established based on randomly small number of quantization bits, even merely one bit of quantization information exchange between each pair of adjacent agents can realize target tracking. Adding the saturation function guarantees the perfect tracking property within a limited time period as the iterations tend to infinity. Simulation results are provided to verify the effectiveness of the proposed approach.