Quantized iterative learning control for nonlinear multi-agent systems with initial state error (Q6569384)

In this paper, the quantized tracking problem for the leader following nonlinear multi-agent systems (MAS) with logarithmic quantization is considered. To weaken the common assumption of initial reset and avoid the complexity of initial-state learning, the authors consider the tracking problem under the assumption of arbitrary value for every agent. To deal with the nonlinearity and uncertainty caused by the system itself and quantization as well as initial state, the learning control is combined with control input compensation. Since quantization causes the system to be right-hand discontinuous, the non-smooth Lyapunov stability theory for the consensus analysis is used. Through the analysis, it is found that the logarithmic quantizer can achieve accurate tracking of the MAS while making full use of the limited communication resources. The effectiveness of the designed protocol is illustrated by some numerical simulations.