Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems
(2021)
Journal Article
Zhao, H., Peng, L., & Yu, H. (2022). Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems. Applied Mathematics and Computation, 412, Article 126582. https://doi.org/10.1016/j.amc.2021.126582
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, a quantized distributed model-free adapt... Read More about Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems.