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Inverse kinematics resolution method of permanent magnetism spherical electric motor on the basis of neural network

A neural network and inverse kinematics technology, applied in the field of inverse kinematics solution using neural network, can solve problems such as difficult solution and complex calculation, and achieve the effect of guaranteed network structure, high convergence accuracy and easy access

Inactive Publication Date: 2012-05-23
TIANJIN UNIV
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  • Application Information

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Problems solved by technology

However, this method is computationally complex and difficult to solve

Method used

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  • Inverse kinematics resolution method of permanent magnetism spherical electric motor on the basis of neural network
  • Inverse kinematics resolution method of permanent magnetism spherical electric motor on the basis of neural network
  • Inverse kinematics resolution method of permanent magnetism spherical electric motor on the basis of neural network

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Embodiment 1

[0032] The permanent magnet spherical motor has the characteristics of simple structure, small size, light weight, low loss, high force index, and easy control. It can be applied to precision devices such as robot joints for multi-dimensional motion in space. Its mechanical structure diagram and the schematic diagram of the three-degree-of-freedom movement of the rotor are as follows: figure 1 , figure 2 shown.

[0033] The orientation and motion of the permanent magnet spherical motor rotor can be defined by Euler angles. The kinematics of permanent magnet spherical motor is divided into forward kinematics and inverse kinematics. Among them, the former is to solve the motion of a certain point on the rotor according to the change of the Euler angle in each degree of freedom, which is the problem of solving the mapping from the Euler angle space to the Cartesian space; the latter is the opposite process, which is from The problem of solving the mapping from Cartesian space...

Embodiment 2

[0100] In the following, we investigate the solution of the inverse kinematics of the permanent magnet spherical motor by the neural network under the condition that the initial value of the Euler angle is not zero. Nutating motion is one of the working conditions that can best examine the torque controllability of spherical motors. Given the change trajectory of Euler angle when the permanent magnet spherical motor is doing nutating motion, the spatial trajectory of nutating motion can be obtained according to the forward kinematics. Then use the trained neural network to solve the change of Euler angle according to the space trajectory. Comparing the given Euler angle change trajectory with the Euler angle change obtained by the neural network solution, the comparison results are as follows Figure 5 As shown, among them, Figure 5 (a), (b), and (c) respectively correspond to the comparison of the three Euler angles α, β, and γ.

[0101] Depend on Figure 5 It can be see...

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Abstract

The invention belongs to the technical field of inverse kinematics resolution of a permanent magnetism spherical electric motor, in particular relates to an inverse kinematics resolution method of a permanent magnetism spherical electric motor on the basis of a neural network. A positive direction kinematics model of the permanent magnetism spherical electric motor is determined through deduction, a feedforward neural network is adopted to build a model for the inverse kinematics of the permanent magnetism spherical electric motor, an input layer is provided with three nerve centers, three coodrdinate figures of positions of a rotor of the permanent magnetism spherical electric motor in Cartesian space are respectively inputted, an output layer is also provided with three nerve centers, and numerical numbers of three eulerian angles of the permanent magnetism spherical electric motor are respectively outputted. The number of nerve centers of a hidden layer is undetermined. The method obtains a training sample used for training the feedforward neural network according to the positive kinematics model of the permanent magnetism spherical electric motor, adopts a Levenberg-Marquardt optimizing algorithm and uses the training sample to train the feedforward neural network so as to determine the structure of the neural network, and can avoid the complicated positive kinematics inverse operation.

Description

Technical field: [0001] The invention belongs to the technical field of solving the inverse kinematics of a permanent magnet spherical motor, and relates to a method for solving the inverse kinematics by using a neural network. Background technique: [0002] For a long time, people have conducted extensive and in-depth research on the servo motor control system of one-dimensional motion, and the electric servo system with the servo motor as the core has been widely used. However, in the research and application fields of modern aerospace, military affairs, chemical industry, industrial automation and intelligent robots, it is increasingly necessary to realize multi-degree-of-freedom motion. For precision devices with complex movements such as robots and manipulators, the servo system composed of conventional one-dimensional motors is too complicated. Traditional single-degree-of-freedom drive motors need two or more servo motors to achieve movement with more than two degree...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/02
Inventor 夏长亮郭辰史婷娜
Owner TIANJIN UNIV
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