Dispersed neural robust controlled trajectory tracking algorithm for mechanical arm

A neural robust and trajectory tracking technology, applied in the field of robotics, can solve problems such as large training volume, large control energy, and increased calculation volume, and achieve the effect of improving tracking accuracy and eliminating disturbances

Inactive Publication Date: 2017-09-26
HEFEI UNIV OF TECH
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Problems solved by technology

At present, the commonly used robot manipulator control methods mainly include PID control. Although this control method is simple and easy to implement, it often requires a lot of control energy, and it cannot guarantee that the robot has good static and dynamic performance; adaptive robust control, its The disadvantage is that the huge calculation required for online identification parameters has strict real-time requirements, especially when there are non-parameter uncertainties, it is difficult for adaptive control to ensure system stability; neural network control and f...

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  • Dispersed neural robust controlled trajectory tracking algorithm for mechanical arm
  • Dispersed neural robust controlled trajectory tracking algorithm for mechanical arm
  • Dispersed neural robust controlled trajectory tracking algorithm for mechanical arm

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Embodiment Construction

[0048] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways defined and covered by the claims.

[0049] As shown in the attached figure, for the dynamic model of the manipulator, considering that directly constructing the neural network model for the whole often requires a large amount of calculation, a decentralized recursive neural network model is designed to construct the state equation for each joint of the manipulator , and then design the controller, by adding a robust item in the control rate to offset the mutual disturbance and modeling error between the neural controllers, and finally prove the stability of the designed controller.

[0050] (1) High-order recurrent neural network (RHONN) model construction. The output of the current sequence of the recurrent neural network (RNN) is related to the previous output, that is to say, the n...

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Abstract

The invention discloses a dispersed neural robust controlled trajectory tracking algorithm for a mechanical arm. According to an algorithm flow, the algorithm sequentially comprises constructing a high-order recurrent neural network model, an estimating a nonlinear system by an RHONN model, estimating unknown weight coefficients in the models, designing a dispersed robust neural controller and proving stability. The invention aims to design a neural controller for eliminating disturbances to improve trajectory tracking accuracy of the mechanical arm.

Description

technical field [0001] The invention belongs to the technical field of robots, and specifically relates to a track tracking algorithm for decentralized neural robust control of a manipulator. Background technique [0002] With the rapid development of the robot industry, the requirements for the robot's work indicators are getting higher and higher, so the trajectory tracking accuracy of the manipulator is becoming more and more important. At present, the commonly used robot manipulator control methods mainly include PID control. Although this control method is simple and easy to implement, it often requires a lot of control energy, and it cannot guarantee that the robot has good static and dynamic performance; adaptive robust control, its The disadvantage is that the huge calculation required for online identification parameters has strict real-time requirements, especially when there are non-parameter uncertainties, it is difficult for adaptive control to ensure system sta...

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 胡海兵杨建德崔世林张结文段敬杰
Owner HEFEI UNIV OF TECH
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