Design of Neural Network-Based Adaptive Inverse Dynamics Controller for Motion Control of Stewart Platform

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.