  This work improves the positioning precision of lightweight robots with
series elastic actuators (SEAs). Lightweight SEA robots, along with
low-impedance control, can maneuver without causing damage in uncertain,
confined spaces such as inside an aircraft wing during aircraft assembly.
Nevertheless, substantial modeling uncertainties in SEA robots reduce the
precision achieved by model-based approaches such as inversion-based
feedforward. Therefore, this article improves the precision of SEA robots
around specified operating points, through a multi-input multi-output (MIMO),
iterative learning control (ILC) approach. The main contributions of this
article are to (i) introduce an input-weighted complex kernel to estimate local
MIMO models using complex Gaussian process regression (c-GPR) (ii) develop
Ger\v{s}gorin-theorem-based conditions on the iteration gains for ensuring ILC
convergence to precision within noise-related limits, even with errors in the
estimated model; and (iii) demonstrate precision positioning with an
experimental SEA robot. Comparative experimental results, with and without ILC,
show around 90% improvement in the positioning precision (close to the
repeatability limit of the robot) and a 10-times increase in the SEA robot's
operating speed with the use of the MIMO ILC.
