Synthesis, stability analysis, and experimental implementation of a multirate repetitive learning controller

Introduces a multirate repetitive learning controller with an adjustable sampling rate that can be used as an “add-on” module to further enhance the performance of a feedback control system. As a result of its multirate characteristics, the user can choose a sampling rate to achieve the required performance based on a trade-off between the accuracy and the complexity of the controller. The controller learns the plant input based on the tracking error down-sampled using a weighted averaging filter. The learned control input is subsequently passed to the plant through an arbitrary hold mechanism determined by the user. The paper extends the existing stability results for single-rate repetitive controllers to the proposed multirate scheme. It also provides an explicit procedure for its design and stability analysis. In addition, the proposed multirate controller has been implemented on hardware and its effectiveness in tracking applications has been verified experimentally