Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems

This paper considers the data quantization problem for a class of unknown nonaffine nonlinear discrete-time multi-agent systems (MASs) under repetitive operations to achieve bipartite consensus tracking. Here, quantized distributed model-free adaptive iterative learning control (QDMFAILBC) approach is proposed based on dynamic linearization technology, algebraic graph theory, and sector-bound methods. The doesn’t require each agent’s dynamics knowledge only uses input/output MASs, where coded by logarithmic quantizer before being transmitted. Moreover, we consider both cooperative competitive relationships among agents. We rigorously prove stability scheme analyze effects quantization. Meanwhile, demonstrate that does not affect tracking errors can converge zero with processing scheme, although slows convergence rate. Furthermore, results are extended switching topologies, three simulation studies further validate effectiveness designed method.