Disturbance observer based iterative learning control for robot manipulators
Main Article Content
In this paper, usin a Lyapunov-like function, we derive a disturbance observer based iterative learning control scheme for the trajectory tracking problem of rigid robot manipulator. In this control scheme, the whole control law consists of two parts, the feedback control law, plus an iteratively updated term represents the estimated disturbance. The feedback control law using in this paper is a computed torque control, without compensating for the gravity forces. Using Lyapunov method, the asymptotic stability of the whole system is guaranteed, and the external disturbances with the gravity forces are compensated. Simulation results on the PUMA 560 robot manipulator, show the asymptotic convergence of tracking error, when the Coulomb and Viscous friction is considered as an external disturbance.