Publication Type : Journal Article
Publisher : World Scientific Pub Co Pte Ltd
Source : International Journal of Computational Methods
Url : https://doi.org/10.1142/S021987622142010X
Campus : Chennai
School : School of Engineering
Department : Mechanical Engineering
Year : 2022
Abstract : This research article attempts to design Inverse Dynamics Controller (IDC) to execute the motion tracking of Stewart Platform. In the presence of modeling uncertainties and external disturbances, the closed-loop dynamic equation of IDC with fixed gains becomes nonlinear and configuration-dependent, which compromises the motion tracking accuracy. Further, both modeling uncertainties and external disturbances are unavoidable in real life conditions. To tackle this issue, this article proposes a novel control algorithm by combining IDC with Feed-Forward Artificial Neural Network (FF-ANN) trained using PSO. The proposed modified control algorithm offers superior motion tracking accuracy in comparison with traditional IDC.
Cite this Research Publication : A. K. Jishnu, Dev K. S. Chauhan, Pandu R. Vundavilli, Design of Neural Network-Based Adaptive Inverse Dynamics Controller for Motion Control of Stewart Platform, International Journal of Computational Methods, World Scientific Pub Co Pte Ltd, 2022, https://doi.org/10.1142/S021987622142010X