Ming Zhang

TL;DR: In this paper, a neural network learning adaptive robust controller (NNLARC) is synthesized for an industrial linear motor stage to achieve good tracking performance and excellent disturbance rejection ability.

TL;DR: An LARC control scheme containing adaptive robust control (ARC) term and iterative learning control (ILC) term in a serial structure is proposed for the magnetically levitated planar motor to achieve high-performance tracking even there exist parametric variations and uncertain disturbances.

TL;DR: The proposed Newton-ILC strategy for contouring motion accuracy of precision multiaxial systems can achieve rather excellent contouring performance when compared with individual axis control, conditional cross-coupled control, and cross- coupled iterative learning coordinated control methods.

TL;DR: The results demonstrate that in comparison with traditional position loop CCC method, the proposed scheme can achieve not only nearly perfect contouring error estimation but also obvious promotion of contouring accuracy.

TL;DR: The proposed feedforward compensation strategy with precise reference modification prediction using gated recurrent units (GRU) not only performs as well as iterative learning control, but also does not need any time-consuming iterations, which is valuable for industrial applications.