Recently, Recurrent Neural Network becomes a very popular research topic in machine learning field. Many new ideas and RNN structures have been generated by different authors, including long short term memory (LSTM) RNN and Gated Recurrent United (GRU) RNN ([1],[2]), a number of applications have also been developed among various research labs or industrial companies ([3]-[5]). Most of these schemes, however, are only applicable to machine learning problems, or static systems in control field. In this paper, a new concept of applying one of the most popular RNN approach - LSTM to identify and control dynamic system is to be investigated. Both identification (or learning) dynamic system and design of controller based on identification are going to be discussed. Also, a new concept of using a convex-based LSTM networks for fast learning purpose will be explained in detail. Simulation studies will be presented to demonstrated the new LSTM structure performs much better than conventional RNN and even single LSTM network.