Multi-stage actuators are becoming more common in practice as the desire for larger work areas is combined with demands for higher precision. Systems such as these exist throughout the manufacturing and digital process environments which perform repetitive tasks. Iterative learning control (ILC) is a control strategy suited for such repetitive tasks. However, complex systems like these often exhibit coupling between individual stages. This letter presents a series-hierarchical ILC strategy (SH-ILC) that employs similar coupling between unique ILC algorithms to improve tracking performance in each stage of actuation. A numerical example of a dual-stage actuator (DSA) is provided, and simulation results demonstrate that the proposed SH-ILC achieves improved reference tracking performance over a comparable multivariable ILC approach.
ASJC Scopus subject areas
- Control and Systems Engineering
- Control and Optimization