Operator Analysis-Based Distributed Quantized Bipartite Learning for Singular Nonlinear Switched Delayed MASs With Self-Learning Input Sharing Under Antagonistic Networks

This paper proposes an accelerated bipartite iterative learning control protocol with self-learning input sharing and errors quantitation to address the problem of bipartite consensus for singular nonlinear switched delayed multi-agent systems (MASs) under antagonistic networks. By constructing a quasi-adjacency matrix, each agent can automatically detect and communicate input information in the form of weights among antagonistic networks, and a novel accelerated learning method based on exponential gain is adopted. Additionally, a logarithmic quantizer is added to the aforementioned protocol to saving communication resources. Then, the above protocol is extended to the bipartite formation problem under antagonistic networks and the scenarios where the topology changes randomly with the number of iterations. Based on the operator theory, the sufficient conditions for its convergence are given, and the convergence of the self-learning input sharing iterative learning control protocol is strictly analyzed. Through three simulation experiments, the efficacy of the protocol is further validated, and it is proved that the proposed protocol has faster convergence speed than the traditional iterative learning control protocol.