Zebin Yang

Overview of Bearingless Permanent-Magnet Synchronous Motors

Bearingless permanent-magnet (PM) synchronous motors (BPMSMs) are a new type of machines combining the characteristics of conventional PM synchronous motors and magnetic bearings. With ever-increasing concerns on applications, such as high-speed electric machines, canned pumps, high-speed-precision mechanical processing, aeronautics and astronautics, flywheel energy storage, life science, vacuum technique, and semiconductor and biotechnology industries, there is a fast growing interest in BPMSMs. In this paper, after a general description of the generation principle of radial suspension forces, a comprehensive overview of BPMSMs is presented, with particular emphasis on mathematical models, motor topologies, and control strategies. Moreover, several possible development trends of the BPMSM are also discussed.

Speed-Sensorless Vector Control of a Bearingless Induction Motor With Artificial Neural Network Inverse Speed Observer

To effectively reject the influence of speed detection on system stability and precision for a bearingless induction motor, this paper proposes a novel speed observation scheme using artificial neural network (ANN) inverse method. The inherent subsystem consisting of speed and torque winding currents is modeled, and then its inversion is implemented by the ANN. The speed is successfully observed via cascading the original subsystem with its inversion. The observed speed is fed back in the speed control loop, and thus, the speed-sensorless vector drive is realized. The effectiveness of this proposed strategy has been demonstrated by experimental results.

High-Performance Control for a Bearingless Permanent-Magnet Synchronous Motor Using Neural Network Inverse Scheme Plus Internal Model Controllers

This paper proposes a novel decoupling scheme for a bearingless permanent-magnet synchronous motor (BPMSM) to achieve fast-response and high precision performances and to guarantee the system robustness to the external disturbance and parameter uncertainty. The proposed control scheme incorporates the neural network inverse (NNI) method and 2-degree-of-freedom (DOF) internal model controllers. By introducing the NNI systems into the original BPMSM system, a decoupled pseudo-linear system can be constituted. Additionally, based on the characteristics of the pseudo-linear system, the 2-DOF internal model control theory is utilized to design extra controllers to improve the robustness of the whole system. Consequently, the proposed control scheme can effectively improve the static and dynamic performances of the BPMSM system, as well as adjust the tracking and disturbance rejection performances independently. The effectiveness of the proposed scheme has been verified by both simulation and experimental results.

Internal Model Control for a Bearingless Permanent Magnet Synchronous Motor Based on Inverse System Method

To effectively enhance the control accuracy and dynamic performance of a bearingless permanent magnet synchronous motor (BPMSM), this paper presents a novel control scheme combining the inverse system method and the internal model control. By cascading the inverse model of the BPMSM with the original BPMSM system, a decoupling pseudo-linear system is constituted. Moreover, in order to improve the robustness of the whole system and reject the influence of the unmodeled dynamics and system noise to the decoupling control accuracy, the internal model control scheme is employed for the pseudo-linear system to design extra closed-loop controllers. Consequently, the proposed decoupling control scheme incorporates the advantages of both the inverse system method and the internal model control. The effectiveness of the proposed control scheme is verified by experimental results at various operations.

Application of LSSVM inverse in a variable frequency induction motor drive system

To improve effectively the dynamic performance and control accuracy of the variable frequency induction motor drive system (VFIMDS), which is a non-linear, strong-coupled and complex system, a novel linearization control method for the speed regulation problem is proposed in this paper. The new control strategy, named the least squares support vector machine (LSSVM) inverse, is based on the inverse system theory and the principle of LSSVM regression. The LSSVM inverse is composed of a LSSVM approximating the inverse model of the VFIMDS and an integrator. Firstly, the mathematical model of the VFIMDS is given and its invertibility is proved. Secondly, the inverse model of the VFIMDS is obtained by using the LSSVM. Thirdly, by combining the LSSVM inverse model with the original VFIMDS, a composite pseudo-linear system can be completed. Then, a linear close-loop adjustor is design to obtain the good speed regulating performance. Finally, simulation comparisons are carried out using the proposed method and the conventional proportional integral control method, and the results demonstrate that the variable frequency speed-regulating performances of the VFIMDS can be greatly improved by using the proposed method.

Nonlinear flux linkage modeling of a bearingless permanent magnet synchronous motor based on AW-LSSVM regression algorithm

Considering the nonlinear magnetization characteristic of bearingless permanent magnet synchronous motors (BPMSMs), this paper describes a novel nonlinear model of the flux linkage for BPMSMs using adaptive weighted (AW) least square support vector machine (AW-LSSVM) regression algorithm. The inputs of the AW-LSSVM are the rotor angle, and torque winding current and suspension force winding current, and the output of the AW-LSSVM is the flux linkage of the BPMSM. Firstly, the LSSVM regression algorithm is used to build the model according to the sample data and obtain the fitting errors of the sample data. Thus, the initial error weights can be calculated on the basis of the fitting errors. And then, the lever weights can be defined based on the space distribution of the sample data. Secondly, according to the error weights and lever weights, the sample weights can be obtained adaptively by the proposed weight value iterative scheme. Finally, the relation between inputs and output is trained and the accuracy AW-LSSVM model of flux linkage can be gained. To compare the property of the proposed flux linkage model, the AW-LSSVM and the LSSVM are employed to build the flux linkage model. The simulation results indicate that by using the proposed AW-LSSVM regression algorithm, the influence of the unavoidable outliers on the model property can be effectively eliminated and the superior performance in high precision, strong robustness and quick convergence can also be obtained.

Nonlinear Model Analysis and “Switching Model” of AC–DC Three-Degree-of-Freedom Hybrid Magnetic Bearing

The most widely studied linear models of the suspension forces in ac-dc three-degree-of-freedom hybrid magnetic bearings may not be practical for application to the precision control of bearings with larger air gaps for larger suspension forces. In addition, the simplified linear and the real nonlinear models in linear zone of current and displacements have not been compared before. In this study, the two most commonly used nonlinear models are analyzed comprehensively to address the lack of nonlinear model analysis in previous research and to seek the most appropriate model for both nonlinear and linear zones (full zone). The results show that no single model completely approximates the test results over the entire zone. A composite model, such as the “switching model,” is more accurate. To illustrate the applicability of the results to both nonlinear model analysis and the proposed “switching model,” nonlinear stiffness and performance test experiments were conducted. It is concluded that the model analysis and the proposed “switching model” can provide a control system with the most suitable mathematical models of the suspension force.

Rotor Mass Eccentricity Vibration Compensation Control in Bearingless Induction Motor

In the process of motor rotation, the vibration caused by the rotor mass eccentricity seriously affects the dynamic characteristics and safety operation of system. So rotor mass eccentricity vibration compensation control on rotating machine has great significance, especially for the high speed bearing less induction motor (BIM). A rotor mass eccentricity compensation control strategy was presented to restrain the vibration of suspended rotor for BIM. Firstly, the suspension rotor dynamical model was deduced and unbalanced vibration mechanism was analyzed. Secondly, based on decoupling control between electromagnetic torque and radial force, the obtained vibration signal from the displacement sensor was put into the original radial force control system. Then, a feedforward compensator was set up to increase the same period component of the given radial force signal and enlarge the stiffness of the vibration signal. Finally, the compensation control of rotor vibration was realized by forcing the rotor shaft rotation around its geometric center. The simulation results show that the presented feedforward compensator can suppress the vibration of rotor under different speed and improve the precision of rotor suspension. The further experimental results also show that the control method can obviously reduce the peak-peak value of rotor radial displacement and effectively restrain rotor vibration.

Overview of Bearingless Induction Motors

Bearingless induction motors combining functions of both torque generation and noncontact magnetic suspension together have attracted more and more attention in the past decades due to their definite advantages of compactness, simple structure, less maintenance, no wear particles, high rotational speed, and so forth. This paper overviews the key technologies of the bearingless induction motors, with emphasis on motor topologies, mathematical models, and control strategies. Particularly, in the control issues, the vector control, independent control, direct torque control, nonlinear decoupling control, sensorless control, and so forth are investigated. In addition, several possible development trends of the bearingless induction motors are also discussed.

Modeling and control of a bearingless permanent magnet synchronous motor

This paper presents a suspension force modeling method of BPMSMs based on Maxwell stress tensor method. By deducing the magnetic flux density of the 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. Furthermore, in order to confirm the performance of the BPMSM and the magnetic suspension in real time, a prototype machine and its control scheme is set up and then corresponding experimental study is carried on. Rotor stabilization with magnetic levitation by using Maxwell tensor modeling method is verified by the simulation and experimental results.