This work demonstrates performance improvement in motion control under a particular set of machine system constraints. A high performance industrial magnetic micro-manipulation system, the Minimag, is introduced and modeled with both first principles and system identification. The form of the closed loop controller is constrained by operational bounds and the system software, resulting in limits to achievable performance. A model-based motion control enhancement is developed and implemented using tools from Iterative Learning Control. The resulting performance improvements indicate the benefits of motion control even when the closed loop controller is fixed. Experimental results at two different size scales (500 μm and 4.5μm) are given.