Kinematic Control of Redundant Manipulators Using Neural Networks

By Shuai Li, Yunong Zhang, Long Jin. IEEE Transactions on Neural Networks and Learning Systems, 2017

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Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks (2018, methodology): “In addition to the applications in which RNNs are used to process time sequences, model-based RNNs that are training free have been successfully applied to different problems such as the control of redundant manipulators [47, 48].”
Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks (2017, methodology): “By contrast, as shown in the table, even without the ability to maximize the manipulability during the task execution process, these QP-based schemes presented in [12], [29], [30] could generate an approximated solution with no feasible solution at the cost of large position errors.”
Accurate photovoltaic power forecasting models using deep LSTM-RNN (2017, background): “…applications, such as optimal demand response in smart grids [54], control of single-phase converters [17], manipulator con-trol [27, 28, 30, 31], modeling crack growth of aluminum alloy [56], distributed task allocation of multiple robots [26], text recognition [43], and localization of…”