Multiple parameter-dependent robust control of miniaturized optical image stabilizers

This paper presents the design of multiple parameter-dependent robust controllers for mass-produced miniaturized optical image stabilizers (OIS's), which are used to minimize the image blur in mobile devices caused by hand-induced camera shake. The dynamics of batch-fabricated OIS's with inevitable product variations is represented by a set of linear models, parameterized by two product-dependent natural frequencies and one uncertain gain. It turns out that the natural frequencies for each OIS product are difficult to determine accurately, and thus assumed to be estimated with errors. The controller is designed to be parameter-dependent on estimated natural frequencies, as well as to be robust against both estimation errors of the natural frequencies and the gain uncertainty. Experimental results on large-scale prototypes demonstrate that the proposed controller outperforms a conventional parameter-independent robust controller as well as a single parameter-dependent robust controller. Specifically, the proposed controller yields more than 27% improvement over the conventional robust controller in terms of lens-tilting tracking performance.