In this paper, we present an adaptive partial state-feedback repetitive learning control algorithm for a rigid-link electrically-driven (RLED) robot manipulator actuated by brushed DC (BDC) motors. The proposed controller is designed to compensate for repeatable mechanical uncertainty via a learning control term while an adaptive control loop is used to compensate for parametric uncertainty in the electrical dynamics. The proposed controller guarantees semi-global asymptotic link position tracking while only requiring measurements of link position and electrical winding current (e.g. measurements of link velocity are not required). © 1996 Taylor and Francis Group, LLC.