Mechanical arm flexible joint control method based on fuzzy neural network

A fuzzy neural network and control method technology, applied in the field of robot dynamics control, can solve problems such as difficulty in obtaining control effects, complex and changeable working conditions of the manipulator, etc.

Active Publication Date: 2018-07-17
CHINA NORTH VEHICLE RES INST
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The working conditions of the manipulator are complex and changeable, and there are factors such as geometric nonlinearity. The dynamic parameter

Method used

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  • Mechanical arm flexible joint control method based on fuzzy neural network
  • Mechanical arm flexible joint control method based on fuzzy neural network
  • Mechanical arm flexible joint control method based on fuzzy neural network

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Embodiment 1

[0038] The embodiment of the present invention proposes a design and implementation method of a fuzzy neural network controller based on the Takagi-Sugeno model. Aiming at the time-varying characteristics of joint stiffness and joint friction nonlinearity, it ensures good control effects under different working conditions.

[0039] Such as figure 1 Shown, the establishment of the fuzzy neural network controller based on the Takagi-Sugeno model of the embodiment of the present invention comprises 4 steps, and step 1 is to set up the fuzzy neural network structure based on the Takagi-Sugeno model, and concrete steps include:

[0040] Step 1-1: Set the first layer of the antecedent network as the input layer, determine the parameters of the input layer, and transmit the input value vector to the second layer of the antecedent network, wherein each node of the input layer is directly connected to the input vector The components of x i connect;

[0041] Each of its nodes is dire...

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Abstract

The invention provides a mechanical arm flexible joint control method based on a fuzzy neural network. The method comprises the following steps that a fuzzy neural network model is established, a neural network parameter learning algorithm is developed; the learning algorithm determines a connection weight parameter of a consequent network and a subordinating degree function center value and a width parameter of an antecedent network; and a fuzzy neural network controller is established according to an identification model to overcome the influence of many nonlinear characteristics of a flexible joint. According to the provided technical scheme, two kinds of control methods of fuzzy logic and the neural network are combined, and the fuzzy neural network controller base on an X model is adopted, so that the neural network is provided with a structure of a fuzzy system, each layer and each node of the neural network correspond to a part of the fuzzy system, the network is different fromblack box operation of a general neural network, all parameters are of definite physical significances, and the network can adapt to the characteristics such as time varying of rigidity and nonlinearfriction of the flexible mechanical arm joint.

Description

Technical field: [0001] The invention relates to the field of robot dynamics control, in particular to a method for controlling flexible joints of a manipulator based on a fuzzy neural network. Background technique: [0002] The mechanical arm joint as the movable part of the mechanical arm is very important for the precise control of the positioning of the mechanical arm. In this year, due to the advantages of large reduction ratio and compact structure, more and more flexible transmission components such as harmonic reducers are used in mechanical Arm joint transmission. However, at this stage, in-depth research on the joints of the manipulator, especially the flexible joints containing the coercive reducer and other flexible transmission mechanisms, such as the in-depth research on the modeling of nonlinear phenomena such as flexibility and friction inside the joints of the manipulator, is needed. Precise control over flexible joints. The working conditions of the manip...

Claims

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Application Information

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IPC IPC(8): B25J9/16
CPCB25J9/161B25J9/1641
Inventor 杨天夫赵洪雷姚问王超江磊蓝伟苏波
Owner CHINA NORTH VEHICLE RES INST
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