Neural Integral Non-Singular Fast Terminal Synchronous Sliding Mode Control for Uncertain 3-DOF Parallel Robotic Manipulators

Robotics have been substituting humans increasingly and effectively to operate repeated, dangerous, heavy, complicated works in human life, production industry, and discovery missions. This work designs a Neural Integral Non-singular Fast Terminal Synchronous Sliding Mode Control (NINFTSSMC) approach for 3-DOF parallel robotic manipulators with uncertain dynamics, using synchronous nonlinear sliding surface, where this sliding surface is formed through the integration of the Synchronization Control (SC) and the Integral Non-singular Fast Terminal Sliding Mode Control (INFTSMC). Accordingly, position errors and synchronization errors quickly converge to the sliding surface at the same time. Next, a Feed-Forward Neural Network (FNN) is applied to estimate uncertain dynamics, whose novelty, compared to a classic FNN, is that they utilize a Non-singular Fast Terminal Sliding Mode (NFTSM) error filter to replace a classic error filter. Thanks to this procedure, the lumped uncertain dynamics are compensated more quickly and more accurately, thus, the malfunction in the reaching phase of state variables approaching the sliding surface is handled thoroughly. Finally, the control approach is designed for a robotic system to achieve the prescribed performance, obtaining rapid error convergence, robustness with uncertain dynamics, minimum chattering, synchronization, and high precision. The stability of the control loop is secured according to the Lyapunov theory. To test the robustness and confirm the effectiveness of the suggested controller for a 3-DOF parallel manipulator, computer simulations and performance comparisons are conducted.