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. The NNLARC scheme contains parametric adaption part, robust feedback part, and radial basis function (RBF) neural network (NN) part in a parallel structure. The adaptive part and the robust part are designed based on the system dynamics to meet the challenge of parametric variations and uncertain random disturbances. It must be noted that in actual industrial machining situations, precision motion equipment is always disturbed by unknown factors, which usually cannot be described by mathematical models but affect the tracking accuracy significantly. Therefore, the RBF NN part is employed to further approximate and compensate the complicated disturbances with high reconstructing accuracy and fast training rate. The stability of the proposed NNLARC strategy is analyzed and proved through the Lyapunov theorem. Comparative experiments under various external disturbances such as completely unknown disturbance added by polyfoam are conducted on an industrial linear motor stage. The experimental results consistently validate that the proposed NNLARC control strategy can excellently meet the challenge of complicated disturbance in practical applications. The proposed scheme also provides a guidance for control strategy synthesis with both good tracking performance and disturbance rejection.

Magnetically levitated planar motor is a new-generation motion device in modern precision industry, while its advanced motion controller design is still of main concern. In this paper, a learning adaptive robust control (LARC) motion controller is proposed for a magnetically levitated planar motor developed in our laboratory to achieve good tracking performance. The planar motor consists of a Halbach permanent magnetic array as the stator, and a levitated platen containing four groups of three-phase windings as the mover. Based on the Lorentz force law, the mover placed in the magnetic field is subjected to vertical force for levitation and horizonal force for planar motion through dynamics decoupling and current allocation. An LARC control scheme containing adaptive robust control (ARC) term and iterative learning control (ILC) term in a serial structure is then proposed for the magnetically levitated planar motor to achieve high-performance tracking even there exist parametric variations and uncertain disturbances. Comparative experiments between traditional lead, ARC, ILC, and the proposed LARC are carried out on the planar motor to track sinusoidal, point-to-point, and planar circular motions, respectively. The experimental results consistently validate that the proposed LARC control strategy outperforms other controllers much, and possesses not only good transient/steady-state tracking performance but also parametric adaptation ability and uncertain disturbance robustness. The proposed scheme actually provides a practically effective technique for motion control of magnetically levitated planar motors in industrial applications.

This paper proposes a Newton extremum seeking algorithm based iterative learning coordinated control (Newton-ILC) strategy for contouring motion accuracy of precision multiaxial systems. Specifically, as the contouring error estimation is critically important for coordinated contouring motion control, a cost function is constructed based on the reference contour, as well as the current position, and the minimal value of the function is searched to determine the contouring error point through an offline Newton algorithm. Consequently, high precision estimation of the contouring error can be achieved, even under extreme contouring tasks with high speed, large curvature, and sharp corners. The calculated contouring error is then projected to each axis, and the axial contouring errors are controlled by iterative learning method, while the learning results will be used to adjust the axial position reference commands for contouring accuracy improvement. Comparative experiments are conducted on a biaxial linear motor stage to validate the practical effectiveness of the proposed Newton-ILC strategy. The experimental results illustrate that the proposed Newton-ILC achieves not only nearly perfect contouring error estimation but obvious improvement of contouring accuracy as well after a few iterations. Particularly, in some extreme cases such as large initial tracking error, high speed, large curvature, and sharp corners, the proposed Newton-ILC strategy can achieve rather excellent contouring performance when compared with individual axis control, conditional cross-coupled control, and cross-coupled iterative learning coordinated control (CCILC) methods.

In a sinusoidal phase-modulating interferometer, sinusoidal modulation of the phase of the laser or the reference wave is necessary. However, modulation of the phase also involves an intensity modulation of the light, which leads to a measurement error if conventional signal processing is used. In addition, the error of modulation depth and the phase delay of demodulation also increase the measurement error. A novel signal processing, with ellipse fitting and a correction method, is proposed. Numerical simulation results and experimental results prove that the novel signal processing can compensate for the measurement error caused by the intensity modulation, the error of modulation depth, and the phase delay of demodulation.