A unified data-driven design framework of optimality-based generalized iterative learning control

• A unified design is proposed for optimal generalized iterative learning control.
• No any explicit model information is used in the controller design.
• The learning gain can be iteratively regulated by using the I/O data only.
• Only the errors at specific points are used in the DDOPTPILC and DDOTILC methods.
• A better performance is achieved by removing the unnecessary specifications.

This paper proposes a unified design framework for data-driven optimality-based generalized iterative learning control (DDOGILC), including data-driven optimal ILC (DDOILC), data-driven optimal point-to-point ILC (DDOPTPILC), and data-driven optimal terminal ILC (DDTILC). First, a dynamical linearization in the iteration domain is developed. Then three specific DDOGILC approaches are proposed. Both design and analysis of the controller only require the measured I/O data without relying on any explicit model information. The optimal learning gain can be updated iteratively, which makes the proposed DDOGILC more adaptable to the changes in the plant. Furthermore, the proposed DDOPTPILC and DDOTILC only depend on the tracking error at specific points, and thus they can deal with the scenario when the system outputs are measured only at some time instants. Moreover, the proposed DDOPTPILC and DDOTILC approaches do not need to track the unnecessary output reference points so that the convergence performance is improved.