Changliang Xia

Research on Torque Calculation Method of Permanent-Magnet Spherical Motor Based on the Finite-Element Method

In this paper, the finite-element method (FEM) is used to calculate the spinning torque of the permanent-magnet (PM) spherical motor. Three-dimensional (3-D) FE model of the PM spherical motor is established. Spinning torque distribution on the spherical surface and its variation curve on the equator are obtained respectively. In order to avoid the complicated torque calculation process under 3-D magnetic field and thus reduce the computational burden, the torque calculation method based on the 2-D conversion model is proposed. This method equivalently simplifies the magnetic field of the spherical PMs and the shape of cylindrical stator windings to be simulation parameters of the 2-D conversion model. With these parameters, 2-D conversion model of the PM spherical motor is established. Spinning torque variation curves obtained by the 3-D model and the 2-D conversion model respectively are compared and the results agree extremely well. By comparing the maximum static torque (MST) obtained under different configuration parameters of the PM spherical motor, it is found that the errors are within the allowable range. Therefore, the reliability of the proposed torque calculation method in the paper is verified. Finally, based on the 2-D conversion model, variation curves of the MST with the length of the air gap, the ampere turns, the length of stator windings and the outer radius of stator windings are obtained, and they are validated by those based on the 3-D model. These results can provide the basis for the optimization of the PM spherical motor.

A New Rapid Nonlinear Simulation Method for Switched Reluctance Motors

This paper presents a new rapid nonlinear simulation method for switched reluctance motors (SRMs). An exact mathematical relationship among the magnetization curves of an SRM is obtained by curve fitting the variable parameters in the Torrey model. Only geometry-based requirements are taken as input needed. Since complete magnetic characteristics are not necessary, a lot of preliminary measurements and calculations are eliminated. The dynamic performances under two operating conditions are predicted using the proposed method, and the measured results are obtained by an experimental laboratory setup. Furthermore, predictions using other two traditional models are presented for the purpose of comparison. Experimental and simulation results verify the accuracy and rapidity of the proposed method.

Speed Control of Brushless DC Motor Based on Single Neuron PID and Wavelet Neural Network

The brushless DC motor (BLDCM) is a multi-variable and non-linear system, so it is difficult to get a satisfying result for BLDCM using the conventional linear control method. This paper presents an approach of single neuron PID adaptive control for BLDCM based on wavelet neural network on-line identification. The method uses single neuron PID to construct the adaptive controller of BLDCM. In addition, a wavelet neural network (WINN) is built to construct the on-line reference model of BLDCM, and then identify the output of the motor. The single neuron PID controller achieves on-line regulation of controller parameters by self learning algorithm. And the identification network provides the gradient information needed by the algorithm. In this paper, a TMS320F2812 digital signal processor (DSP) is used to implement this control scheme. And the experimental result shows that the method proposed by this paper can achieve on-line identification and on-line control with high control accuracy, good static and dynamic characteristic and strong robustness.

Single Neural PID Control for Sensorless Switched Reluctance Motor Based on RBF Neural Network

This paper presents a novel approach of single neuron PID control for position sensorless switched reluctance motors (SRM) based on radial basis function (RBF) neural network. In the proposed RBF neural network, there is no hidden units at the beginning, and during the process of learning, they are increased or decreased according to an adaptive algorithm. So the RBF neural network is built with a much simpler and tighter structure to form an efficient nonlinear map, and then it facilitates the elimination of the position sensors. Moreover, the paper uses single neuron to construct the adaptive controller of SRM, which has the advantages of simple structure, adaptability and robustness. A RBF network is built to identify the system on-line, and then constructs the on-line reference model, implements self-learning of controller parameters by single neuron controller, thus achieve on-line regulation of controller parameters. The experimental result shows that the method given in this paper can construct process model through on-line identification and then give gradient information to neuron controller, it can achieve on-line identification and on-line control with high control accuracy and good dynamic characteristics

Adaptive PWM Speed Control for Switched Reluctance Motors Based on RBF Neural Network

The switched reluctance motor drive (SRD) has obtained great attention as an AC stepless speed control system due to its large regulating scope, low cost and ruggedness. However, its strong nonlinearity and multivariable characteristic make it difficult to control. To solve the problem, this paper presents an approach of adaptive PWM speed control for switched reluctance motors (SRM) based on RBF neural network. This method builds up a speed controller based on RBF neural network which has powerful approximating ability and fast convergence property. The controller is trained off-line in advance, and then with the motors operation, the on-line training of it makes its parameters vary with the environment in order to improve the control performance. In addition, another RBF network is constructed to offer gradient parameters, which is needed by the on-line training, via on-line identification. The results of experiments prove that the approach has lots of advantages in response speed, control accuracy and adaptability

Control of Brushless DC Motor Using Fuzzy Set Based Immune Feedback PID Controller

Brushless DC motors (BLDCM) are reliable, easy control, and inexpensive. But, the brushless DC motor is a multi-variable and nonlinear system. Conventional PID controllers suffer from uncertain parameters and the nonlinear of the BLDCM. This paper presents an novel approach of immune feedback PID control for BLDCM based on fuzzy set. The method is inspired by biological immune feedback mechanism functioned by T-cells, including an active term, which controls response speed, and an inhibitive term, which controls stabilization effect, and we employ a fuzzy logic to implement the inhibitive term. The system includes current and velocity closed loops. The simulation illustrates that excellent flexibility and adaptability as well as high precision and good robustness are obtained by the proposed strategy.

Adaptive Speed Control for Brushless DC Motors Based On Genetic Algorithm and RBF Neural Network

The brushless DC motors (BLDCM) are a multi-variable and non-linear system, so the research about the high performance of BLDCM is important, especially the control methods based on neural network. To solve the deficiency of neural network such as decision of structure and adjustment of parameters in hidden-unit, this paper presents an adaptive speed control approach based on genetic algorithm tuning radial basis function (RBF) neural network controller for brushless DC motor. In this approach, the RBF neural network whose structure and parameters of hidden-unit have been trained by genetic algorithm off-line constitutes a speed loop controller. The controller tunes parameters of neural network adaptively via the self-modifiability of network on-line, while the motor is running. At the same time, the current loop controller traces the change of given current rapidly, so that the system can adapt to variational environment. The results of experiments prove that the approach has lots of good performances in response speed, control accuracy, adaptability and robust.

Rotor Position Estimation for Switched Reluctance Motor Using Support Vector Machine

Switched reluctance motor (SRM), which has simple construction, high reliability, high efficiency and low cost, has shown its strong competition in many fields. However, mechanical position sensors add to the cost, complexity and potential unreliability at high speed. This paper presents an approach of rotor position estimation for switched reluctance motor based on support vector machine (SVM). For the nonlinear property of SRM, this approach takes advantage of SVM with better solution for small-sample learning problem and well generalization property. Through the off-line training, a better support vector machine structure in which phase current and phase flux linkage are inputs and the corresponding position is the output, is built with to form an efficient nonlinear mapping, and then it facilitates the rotor position estimation. The simulation and experimental results show that this method can achieve correct rotor position estimation, and thus the sensorless control of SRM is realized.

A current control algorithm based on variable current threshold for four-switch three-phase BLDCM using intelligent controller

Cost minimization is the key to the large volume manufacture and application of the brushless DC motor. A novel four-switch three-phase brushless DC motor drive based on variable threshold direct current control is proposed to lower cost and improve performance. The real-time current threshold identifier working based on speed error and current of DC link, is made up of a radial basis function neural network. The current threshold value is adjusted synchronously with phase conversion to make sure that the phase current wave could keep in shape of trapezoid. Two single neuron PID controllers consist of the current controller. They individually regulate duty cycle of PWM signals working on the inverter bridge to make phase current fall in the specified threshold quickly and smoothly. Experimental system has the advantages of flexible control, strong adaptability, and achieves higher control accuracy and better robustness. Also the structure of system is simplified.

Brushless DC motor sliding mode control with Kalman Filter

A brushless DC motor (BLDCM) state equations based on speed error are established in this paper, and a sliding mode controller with a variable structure based on exponential reaching law is designed for speed regulation of BLDCM. Kalman Filter is used to improve the performance in the situation of noise disturbance. The control performances of variable-structure controller presented in this paper and double close-loop speed regulator are contrasted and analyzed in different situations. From the results of simulation, it can be known that the controller presented in this paper has good performance on many aspects such as response speed, anti-disturbance and so on. Furthermore, the Kalman Filter can effectively weaken controllers chattering and improve the precision of system.