Real-Time Tracking Control and Efficiency Analyses for Stewart Platform Based on Discrete-Time Recurrent Neural Network

In recent years, the discrete-time recurrent neural network (DTRNN) model has received growing attention. This fully benefits from the recurrent neural networks (RNNs) that not only have plenty of advantages for solving computing problems in the real-time tracking control but also have the remarkable potential of parallel processing and nonlinear processing. However, there is a general lack of research on the applicability of DTRNN model to handle parallel robot. In addition, the precision is always an important point in real-time tracking control, and most of existing studies generally lack the elaborate researches on the precision analyses. In this article, the corresponding DTRNN model (i.e., general five-instant discretization (FID) formula DTRNN model) with parameter selection method is established. As one of the important theoretical contributions, the dominant term of truncation error of discretization formula and the conditions of maintaining precision of corresponding DTRNN model are proved from the mathematical view strictly. Besides, the influence of the selected parameter for the precision of such a DTRNN model is also analyzed. Finally, the above theoretical analyses are verified in the tracking control experiments of the Stewart platform, which is a widely used and representative parallel robot.