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

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