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Stewart stable platform control method based on RBF neural network

A stable platform and neural network technology, applied in the field of Stewart stable platform control based on RBF neural network, can solve problems such as synthetic pose errors, overcome assembly errors and model errors, overcome nonlinear factors, improve tracking accuracy and resist The effect of interference ability

Inactive Publication Date: 2019-12-20
NANJING UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
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Problems solved by technology

In fact, due to the assembly error and model error of the actual device, there will be a large error in the actual synthetic pose of the Stewart platform

Method used

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  • Stewart stable platform control method based on RBF neural network
  • Stewart stable platform control method based on RBF neural network
  • Stewart stable platform control method based on RBF neural network

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Experimental program
Comparison scheme
Effect test

Embodiment

[0127] The present embodiment carries out experimental verification to the Stewart stable platform control method based on RBF neural network of the present invention:

[0128] The parameters of the permanent magnet synchronous motor (PMSM) are shown in Table 1 below:

[0129] Table 1 Parameters of PMSM

[0130]

[0131] From the kinematic modeling process of the Stewart platform, it can be found that the main errors include: the installation deviation of the spherical joint of the moving platform ΔX Mi ,ΔY Mi ,ΔZ Mi , the installation deviation ΔX of the base spherical joint Fi ,ΔY Fi ,ΔZ Fi , and the initial zero position deviation ΔL of the drive shaft 0 i. There are 42 installation deviation items of the whole mechanism.

[0132] The installation deviation ΔB of the base ball joint used in this embodiment i =ΔX Fi ,ΔY Fi ,ΔZ Fi for:

[0133]

[0134] The installation deviation ΔP of the spherical joint of the moving platform i =ΔX Mi ,ΔY Mi ,ΔZ Mi fo...

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Abstract

The invention discloses a Stewart stable platform control method based on an RBF neural network, and the method comprises the following contents of establishing a kinematic model of the Stewart stableplatform according to the principle of robot kinematics, and obtaining a Jacobian matrix of the Stewart stable platform about the posture through total differentiation; establishing a dynamic model of the Stewart stable platform by using the Lagrange's equation method and combined with the Jacobian matrix; mathematically modeling the single channel of Stewart according to the dynamic model to obtain a transfer function, and then adopting a PID control algorithm and a series control strategy to control the position of the single channel; constructing a posture control strategy of the Stewart stable platform, and stably controlling the Stewart platform by using the strategy. The method provided by the invention can effectively overcome the assembly error and model error of the Stewart stable platform, and improves the tracking accuracy and anti-interference ability.

Description

technical field [0001] The invention belongs to the technical field of automatic control, in particular to a Stewart stable platform control method based on an RBF neural network. Background technique [0002] The Stewart platform belongs to the spatial multi-ring parallel mechanism. From the structural point of view, it is formed by connecting the upper and lower platforms with 6 rods. These 6 rods can be freely expanded and contracted by hydraulic pressure or electric motor, and they are respectively connected with the upper and lower platforms by Hooke hinges. The upper plane can move in 6 degrees of freedom relative to the lower plane, so the Stewart platform belongs to the six degrees of freedom parallel robot. Its main features are compact structure, high rigidity, fast speed, large bearing capacity, no cumulative error of each joint, and high precision. However, because the Stewart platform is a highly nonlinear, strong coupling, complex dynamic model and multi-inpu...

Claims

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Application Information

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IPC IPC(8): G05B11/42G05B13/02G05B13/04
CPCG05B11/42G05B13/027G05B13/042
Inventor 黄天宇李军李策汪萌余缜
Owner NANJING UNIV OF SCI & TECH
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