A model-free controller for uncertain robot manipulators with matched disturbances

Precise control is critical for robots, but it difficult to obtain an accurate dynamic model of the robot due presence modeling errors and uncertainties in complex working environment, resulting decreased performances. The article proposes a method designing digital controller output tracking disturbed repetitive manipulators that does not require mathematical controlled with goal improving accuracy. This consists two separate intelligent parts. first part aims stabilize original via model-free state feedback linearization technique. suggested technique make use Euler-Lagrange robot. second will then employ concept iterative learning asymptotically drive obtained stable linear system desired references. Both these parts only robot’s measured data from past carrying out their tasks instead models. Moreover, proposed structurally simple computationally efficient. Finally, validate theoretical results, simulation verification on 2-degree-of-freedom (DOF) uncertain planar performed, results show excellent performance feasible.