ILC applied to a flexible two-link robot model using sensor-fusion-based estimates

摘要

Estimates from an extended Kalman filter (EKF) is used in an iterative learning control (ILC) algorithm applied to a realistic two-link robot model with flexible joints. The angles seen from the arm side of the joints (arm angles) are estimated by an EKF in two ways: 1) using measurements of angles seen from the motor side of the joints (motor angles), which normally are the only measurements available in commercial industrial robot systems, 2) using both motor-angle and tool-acceleration measurements. The estimates are then used in an ILC algorithm. The results show that the actual arm angles are clearly improved compared to when only motor angles are used in the ILC update, even though model errors are introduced.