The work is motivated by problems during automated motion control of mobile microrobots. For a high positioning accuracy a data-based learning controller model is required since a microrobot's motion behaviour is difficult to model and since the model parameters even change with time. The approach of the Self-Organising Locally Interpolating Map (SOLIM) includes a new method that continuously and interpretably maps from a grid of input support vectors, e.g. a robot's velocity, to a grid of output support vectors, e.g. corresponding control commands. Moreover, a learning algorithm has been developed, which iteratively adapts the output support vectors such that the SOLIM map represents an inverse model of the unknown system to be controlled. The most important properties of the SOLIM approach have been proven in simulations and during position control of mobile micro-robots.