Performance requirements in precision motion systems, including those used in integrated circuit manufacturing and printing systems, are ever increasing. For instance, internal deformations cannot be neglected anymore. As a result, measured signals at sensor locations cannot be used directly to evaluate performance at the point of interest. The aim of this brief is to develop an inferential motion control framework that explicitly distinguishes between performance variables and measured variables. In the proposed framework, a dynamic model is used to infer the performance variables from the measured variables. As the inferred performance variables depend on the model quality, an identification for robust inferential control approach is pursued that tightly captures the uncertainty. Experimental results on a prototype motion system reveal that ignoring internal deformations using traditional motion control design approaches can lead to disastrous performance at the point of interest. In addition, it is shown that the proposed inferential motion control framework leads to high performance at the unmeasurable point of interest.