Identification of cyclic disturbances in positioning systems via compressive sensing

In industrial precision positioning systems the measurement position is hardly ever the same as the location of the actuator. The properties and imperfections of the actuator and the underlying components between the sensor and the actuator mainly lead to deterministic reproducible position errors. The advantage of these systematic cyclic disturbances is that they can be compensated for, once identified. In this paper we use nonuniform sampling combined with Compressive Sensing (CS) to identify high spatial frequency disturbances in positioning systems when the spatial sample period is limited. The proposed strategy is implemented on the paper positioning unit of a wide format printer, to identify the cyclic disturbances in the positioning of paper with respect to the printheads. Based on CS, we present a strategy to identify the cyclic disturbances in the paper positioning from randomly obtained relative position error measurements. Experiments with a limited spatial sample period show that the high disturbance frequencies are also successfully identified.