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The radial suspension force with a new structure of a bearingless induction motor based on single winding is researched. Compared to the conventional double-winding structure of bearingless induction motor, torque and suspension forces are produced with a single-winding system. Bearingless induction motor is a nonlinear, multi-variable and strong coupling system. It is difficult to obtain an accurate mathematical model on the radial suspension force. So the research method about radial suspension force of a single-winding bearingless induction motor is proposed, based on two fundamentals. Firstly, a new structure and operation principle of a single-winding bearingless induction motor is introduced. Then the air gap flux density distribution of the single-winding bearingless induction motor is analyzed in detail. The accurate mathematical model of radial suspension force is deduced by using two-fundamental wave method and Maxwell's stress tensor method. Secondly, according to the transient analysis of the single-winding bearingless induction motor in which its speed is 6000 r/min by finite element method (FEM), the component of radial suspension force in x-axis and y-axis is obtained by FEM simulation analysis. The calculation results used by FEM and the theoretical calculation results of mathematical model used by two-fundamental wave method have been compared. Thirdly, an experimental prototype is produced, and suspension experiment of prototype is carried out. Then measured result of radial suspension force is analyzed. The analysis results show that the prototype has excellent suspension characteristics, and the mathematical model of radial suspension force based on two-fundamental wave method has low error and high precision.

A novel decoupling control method based on neural networks inverse system is presented in this paper for a bearingless synchronous reluctance motor (BSRM) possessing the characteristics of multi-input-multi-output, nonlinearity, and strong coupling. The dynamic mathematical models are built, which are verified to be invertible. A controller based on neural network inverse is designed, which decouples the original nonlinear system to two linear position subsystems and an angular velocity subsystem. Furthermore, the linear control theory is applied to closed-loop synthesis to meet the desired performance. Simulation and experiment results show that the presented neural networks inverse control strategy can realize the dynamic decoupling of BSRM, and that the control system has fine dynamic and static performance.

The bearingless synchronous reluctance motor (BSynRM) is a multi-variable, nonlinear and strong-coupled system. To solve the difficult problem of precise decoupling in electric torque and radial suspension force control, the theory of direct torque control of traditional synchronous reluctance motor are applied to the torque control of a BSynRM in this paper. Based on mathematical models of the BSynRM, A direct torque control algorithm based on space vector pulse width modulation (SVM-DTC) is deduced. The SVM-DTC control system is designed and simulated. The simulation results show the control algorithm of SVM-DTC realizes decoupling in electric torque and radial suspension force control. The static and dynamic performance of electric torque, speed and radial suspension force of the BSynRM is excellent.

In this paper, a novel radial hybrid magnetic bearing is proposed, which is driven by an AC 3-phase power converter, and biased magnetic fluxes are provided by an axial magnetized permanent magnet ring. Based on the introduction of the configuration and principle of the radial AC hybrid magnetic bearing (AC HMB), the methods in parameter design and design results for the prototype are presented. The parameter design results for the prototype of the radial AC HMB are simulated by 3D electromagnetic finite element method and the simulation results show that the obtained parameter is reasonable. The manufactured prototype applying to the parameter is operated and the test results also show that the design result for the prototype is available, and the AC HMB has good dynamic and static performance.

We investigate the influence of grain size on mechanical properties in a Fe-9Ni-12Mn-2.5Si-1.0C TWIP steel by unidirectional tensile. Meanwhile the microstructures of the TWIP steel were observed and analyzed by optical microscope (OM) and transmission electron microscope (TEM). The experimental results show that the TWIP steel’s yield strength and tensile strength decrease with the increasing of grain size, whereas the plasticity increases with it. When the average grain size reaches to 27μm, the tensile strength is 1080MPa, the elongation percentage is 77%, and the strength-plasticity product achieves the 83160MPa•%. Steel’s strain hardening rate can be changed from three-stage to four-stage with the increasing of grain sizes, the areas of strain hardening by twin deformation mechanism are expanded. Through the microstructure observation we found that, coarse-grained TWIP steel conducts to twinning formation, the high density twins can increase the alloy’s ductility by splitting the grain.

A dynamic decoupling control method based on neural network inverse system theory is developed for the 5 degrees of freedom (5-DOF) rotor system. The rotor system suspended by AC hybrid magnetic bearings (HMBs) is a multivariable, nonlinear and strong coupled system. Firstly, the configuration of 5-DOF HMBs and the mathematical equations of suspension forces are set up. Secondly, it is demonstrated the system is reversible by analyzing mathematical model. On the basis, the neural network inverse system which is composed of the static neural networks and integrators, and original system are in series to constitute pseudo linear systems. Finally, linear system theory is applied to these linearization subsystems for designing close-loop controllers. The simulation results show that this kind of control strategy can realize dynamic decoupling control, and control system obtains good dynamic and static performances.

This paper presents a suspension force modeling method for bearingless permanent magnet synchronous motors (BPMSMs) using Maxwell stress tensor method. By deducing the magnetic flux density of airgap in BPMSMs using magnetic circuit analytic approach, the suspension force model with rotor eccentricity is established. And then the suspension force of a surface-mounted BPMSM is computed by the finite element method. The simulation results illustrate the effectiveness of the proposed modeling method.

The problem of roust stabilization for linear time-varying uncertain periodic descriptor systems is revisited. Based on the concept of robust stability for linear time-varying uncertain periodic descriptor systems, a necessary and sufficient condition for robust stability is put forward. The robust stabilization problem is also studied and the corresponding necessary and sufficient condition is given using the notation of dual system. The obtained matrix inequality conditions can be transformed to linear matrix inequality ones with the introduction of some free matrices, which makes the analysis and design procedure simple and reliable.