Modelling an Industrial Robot and Its Impact on Productivity

This research aims to design an efficient algorithm leading improvement of productivity by posing a multi-objective optimization, in which both the time consumed carry out scheduled tasks and associated costs autonomous industrial system are minimized. The proposed models kinematics dynamics robot, provides collision-free trajectories, allows constrain energy meets physical characteristics robot (i.e., restriction on torque, jerks power all driving motors). Additionally, trajectory tracking accuracy is improved using adaptive fuzzy sliding mode control (AFSMC), compensating for parametric uncertainties, bounded external disturbances constraint uncertainties. Therefore, stability robustness enhanced; thus, overcoming some limitations traditional proportional-integral-derivative (PID) controllers. trade-offs among economic issues related assembly line optimal desired motion analyzed Pareto fronts. technique tested different examples six-degrees-of-freedom (DOF) system. Results have proved how use this methodology enhances performance reliability lines.