Magnetic levitation positioning systems have attracted an increasing amount of attention for many modern industrial applications because they can work in a large range of motion and in multiple degrees of freedom (DoFs) with resolutions as small as nanometer and microradian levels. In this paper, a 6-DoF positioning system based on 4 linear magnetic levitation actuators (MLAs) is designed and tested. In contrast to the existing modeling method, this work takes the yaw angle into account in the magnetic force and torque solutions of the linear MLA, and the revised model is employed in motion control. The raw data from the sensing system are processed by the Newton-Raphson method for the position and rotation motions of the translator. The PID compensator controls the equivalent second-order system as the motions in different axes are decoupled by the proposed wrench model. Experimental results related to the motion resolution, motion range, step response, trajectory tracking, and payload capacity are given to evaluate the performance of the prototype. The results show that the stroke of the maglev system is a volume of 20mm×20mm×4mm with a rotational range of 0.05rad×0.05rad×0.2rad and that the positioning precision depends on the resolution of the sensing system.

Considering the increasingly strict motion precision requirements and repetitive task characteristics of magnetic levitation systems (MLSs) in the sophisticated industry, this article proposes a novel learning adaptive sliding mode control (LASMC) strategy for the MLS to achieve an excellent tracking performance. The LASMC scheme is obtained by combining adaptive sliding mode control (ASMC) and iterative learning control (ILC) terms in a parallel structure. ASMC can guarantee system stability and strong robustness, which employs an adaptive switching gain and parameter adaption algorithm. Even if the accurate disturbance bounds of MLS are not known, the ASMC item can also adjust the switching control gain online to ensure the stability and robustness of the system. Additionally, the ILC term can further improve the MLS performance for repetitive motion tasks without the accurate dynamics model. The asymptotic stability of the LASMC strategy is verified based on the Lyapunov theorem in the presence of unmodeled dynamics, disturbances, and saturation. Comparative experiments carried out on a maglev rotary table demonstrate that the proposed control strategy achieves excellent tracking accuracy and disturbance robustness in the practical MLS. The LASMC scheme provides a decent idea for the application of intelligent control technology in the controller design of MLSs.

The magnetically levitated rotary table (MLRT) has the potential to be applied in many industrial applications due to its excellent rotation ability and good positioning accuracy. This paper presents the prototype of a novel MLRT with a simple and compact structure. The table employs a circular permanent magnet (PM) array and a circular coil array to generate large scale rotation around the vertical axis and limited movement in the other directions. This MLRT has a higher current carrying capacity and simpler commutation law than the existing MLRT. Furthermore, different from the previous works, the magnetic force model presented in this paper is obtained by a novel analytical-numerical method that considers the clearances in the circular PM array. Thus, the predicted magnetic force and torque are more accurate than those from the existing modeling method. The translation and rotation of the table are measured by six laser sensors, which are distributed around and below the mover respectively. The PID controller is used to stabilize the system. Experiments related to the motion decoupling, trajectory tracking and load change are carried out to verify the motion performance of the novel rotary table. Compared with the existing magnetic force model, the experimental results illustrate that the novel magnetic force model, which considers the clearances in a circular PM array, is more suitable for motion control of the MLRT.

The existing simulation method for the control of linear or planar magnetically levitated actuators always ignores the characteristics of the real physical object, which deteriorates the accuracy of the simulation. In this work, the proposed emulator for the magnetically levitated actuator is developed to consider both the force characteristics and the control algorithm. To model the real controlled object, the mathematical model for 1D (one-dimensional) and 2D (two-dimensional) magnetic arrays is derived where the yaw angle is taken into consideration using the coordinate transformation. The solution of the mathematical model is compared with the commercial BEM (boundary element method) software and the measurements from a force and torque testing setup to highlight the accuracy of the proposed mathematical model. Compared with the traditional simulation method of the motion control systems founded on the simplified system transfer function, the proposed simulation method has higher consistency and is closer to reality. The accuracy and efficiency of the proposed magnetic force model are further verified by the emulator based on the numerical force model and the testing data of the real setup.

This paper proposes a fast and accurate electromagnetic optimization method for magnetic levitation planar motors (MLPMs). With the purpose of achieving the maximum force with the minimum power dissipation and cost, a multi-objective optimization is adopted for the MLPM. Furthermore, in order to improve the precision and reduce the computational burden, the levitation force expression in the objective function is analyzed through the numerical model. This model is built by the magnetic charge method and the Gaussian quadrature and implemented in parallel computation. According to the optimal objective, an intelligent optimization algorithm--particle swarm optimization (PSO)--is used to undertake the optimization variables of the planar motor for a global optimal solution. The precision and effectiveness of the proposed method are validated by both the finite-element method (FEM) commercial software COMSOL and a boundary element software program named Radia. The comparative results with other optimization methods are also presented to highlight the performance of the proposed optimization method.