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Moreover, the input of each following agent is subject to saturation constraint. It is assumed that the information of leader is not available to any following agents, and the radial basis function neural network is introduced to approximate the nonlinear dynamics. Then, a distributed adaptive neural network iterative learning control protocol and the adaptive updating laws for the time\u2010varying parameters are proposed, respectively. A new Lyapunov function is constructed to analyze the validity of the presented control protocol. Finally, a numerical example is provided to verify the effectiveness of theoretical results.<\/jats:p>","DOI":"10.1155\/2019\/9858504","type":"journal-article","created":{"date-parts":[[2019,5,13]],"date-time":"2019-05-13T23:31:10Z","timestamp":1557790270000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Leader\u2010Following Consensus for Second\u2010Order Nonlinear Multiagent Systems with Input Saturation via Distributed Adaptive Neural Network Iterative Learning 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