Efficient workspace sharing of collaborative robots and human operators remains an unsolved problem in the industry. This problem goes beyond the use of a priori or a posteriori safety measures and has to be tackled at the control level. To address the need of adaptation to human presence as well as to endow the robot with the ability to adapt interactively to new Cartesian targets, a linear Model Predictive Controller is proposed in this paper. This controller computes accelerationbounded optimal Cartesian trajectories in SE(3) over a receding horizon. The pertinence of the proposed control architecture is demonstrated using experiments with the Franka Emika robots in different scenarios implying both adaptation of the maximum allowed velocity to comply with human presence and on-the-fly update of a Cartesian goal pose.