Chuxiong Hu

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 orthogonal global task coordinate frame (GTCF) in which the calculation of contour error is exact to the first-order approximation of the actual contours error, no matter how large the position tracking errors would be.

TL;DR: The recently developed integrated direct/indirect adaptive robust control (DIARC) for a class of nonlinear systems with unknown input dead-zones is combined with the desired compensation strategy to synthesize practical high-performance motion controllers for precision electrical drive systems having unknown dead-zone effects.

TL;DR: A discontinuous-projection-based desired compensation adaptive robust contouring controller to address control issues all at once and achieves a guaranteed transient performance and a steady-state contouring accuracy even in the presence of both parametric uncertainties and uncertain nonlinearities.

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.