Learning complex nonlinear dynamics of a 3D translational parallel manipulator using neural network.

ABSTRACT: This study investigates modeling the dynamics of a 3D translational parallel manipulator with closed chains using feedforward neural networks (FFNNs). The dataset exceeds 50,000 samples, incorporating experimental data collected from a robot prototype using MATLAB® real-time workshop and the National Instruments^TM DAQ toolbox, as well as CAD simulation data from MSC ADAMS software. While achieving satisfactory mean squared error (MSE), some predictions did not fully capture the manipulator's dynamics, with small overfitting observed. A Deep Neural Network (DNN) was tested but faced overfitting and high computational costs, despite being trained on a subset of the dataset. This highlighted the limitations of DNNs for modeling such complicated parallel robots with closed chains an

SUBMITTER: Elgammal AT 

PROVIDER: S-EPMC11416574 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature