Data-based control of motion systems

This paper presents recent research results for feedback control design of motion systems. Two model-free approaches are investigated, that exploit the ease of experimentation which is typical for motion systems. One approach is data-based design of a linear feedback controller which realizes desired closed-loop sensitivity and complementary sensitivity transfer functions. These transfer functions are specified via a data-based performance cost. The designer can prescribe both the controller structure and the complexity. Experimental results obtained in a direct-drive robot motion control problem confirm the effectiveness of the design. A second line of research is unfalsified control where a set of controllers is iteratively tested against measured data. Experimental results for the well-known fourth order benchmark motion system show feasibility of the approach. Finally, we implemented a nonlinear SPAN filter on the same system, which outperforms a linear feedback design.