A HYBRID LEARNING ADAPTIVE PARTIAL STATE-FEEDBACK CONTROLLER FOR RLEDROBOT MANIPULATORS/

In this paper, we present an adaptive partial state-feedback repetitiv e learning control algorithm for a rigid-link electrically-driven (RLE D) robot manipulator actuated by brushed DC (BDC) motors. The proposed controller is designed to compensate for repeatable mechanical uncert ainty via a learning control term while an adaptive control loop is us ed to compensate for parametric uncertainty in the electrical dynamics . The proposed controller guarantees semi-global asymptotic link posit ion tracking while only requiring measurements of link position and el ectrical winding current (e.g. measurements of link velocity are not r equired).