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Study of Dynamics Decoupling Control and Electrifying Strategy of Permanent Magnet Spherical Motor
Author: GuoChen
Tutor: XiaChangLiang
School: Tianjin University
Course: Motor and electrical
Keywords: PM Spherical Motor Decoupling control algorithm Static torque model Power policies Inverse kinematics Neural Networks Ant Colony Algorithm
CLC: TM351
Type: PhD thesis
Year: 2010
Downloads: 120
Quote: 0
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Abstract
This article discusses the major structural features for a new permanent magnet spherical motor, and the motor dynamics modeling, model error and causes. According to the characteristics of a large number of nonlinear coupling term in the kinetic model, this paper presents a decoupling control algorithm based on fuzzy controller dynamics; Subsequently, in order to eliminate the uncertainty in the dynamic model, to further improve the decoupling control effect, a control algorithm based on neural network identification and ANFIS dynamics decoupling. Both control algorithms are used the structure of the computed torque method, to eliminate nonlinear crosscoupling between Euler angle axial improve the dynamic and static performance of the system. The simulation analysis to verify the correctness of the proposed algorithm. In this paper, the stator windings are energized based on spherical planning strategy. First, using the finite element method to get the permanent magnet spherical motor static torque model. Then, the stator windings of the stator sphere coverage area the spherical planning, is divided into four categories of subregions, to describe analytical method for all subregions, and label each stator winding according to the principle of similar triangles. Finally, according to the position of the rotor, with a specific rule to determine the need to energization of the stator winding, and calls the static torque model, the solution to obtain the size of the energizing current. The power policies, permanent magnet spherical motor control algorithm to achieve complex trajectory tracking run. Simulation analysis to verify the reasonableness of the power policies. Analytic solution for the inverse kinematics problem of the permanent magnet spherical motor is more complex. Solve this problem, we propose a method based on neural network inverse kinematics, inverse kinematics model to approximate the motor using neural network; determine the neural network based on neural networks using LM optimization algorithm sample learning effect optimal structure. Subsequently, an inverse kinematics method based on improved ant colony algorithm to validate the superiority of the algorithm, and parameter settings. Build a spherical permanent magnet motor based on DSP TMS320F2812 double closed loop rotation control experimental platform, and hardware circuit of the main part of the discussion. The detection of the rotor position of permanent magnet spherical motor and bus current to achieve the doubleloop speed control. As can be seen from the governor effects permanent magnet spherical motor can achieve a faster start and smooth running.

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