A Neural Network Inversion Control Method for Flexible Manipulator System

A technology of flexible manipulator and inversion control, applied in the field of neural network control

Active Publication Date: 2017-10-13
GUANGZHOU ETON ELECTROMECHANICAL
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Problems solved by technology

Because of the uncertain factors of these parameters, it is more challenging to design the corresponding controller

Method used

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  • A Neural Network Inversion Control Method for Flexible Manipulator System
  • A Neural Network Inversion Control Method for Flexible Manipulator System
  • A Neural Network Inversion Control Method for Flexible Manipulator System

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Embodiment Construction

[0087] The present invention will be further described below in conjunction with the accompanying drawings.

[0088] refer to Figure 1-Figure 5 , a neural network inversion control method for a flexible manipulator system, comprising the following steps:

[0089] Step 1, establish the dynamic model of the servo system of the manipulator, the process is as follows:

[0090] 1.1 The expression form of the dynamic model of the manipulator servo system is

[0091]

[0092] Among them, q and θ are the angles of the manipulator connecting rod and the motor, respectively; g is the acceleration of gravity; I is the inertia of the connecting rod; J is the inertia of the motor; K is the spring stiffness coefficient; Length; u is the control signal;

[0093] define x 1 =q, x 3 = θ, Formula (1) is rewritten as

[0094]

[0095] Among them, y is the system output trajectory;

[0096] 1.2 Define the variable z 1 =x 1 ,z 2 =x 2 , Then formula (2) can be rewritten as...

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Abstract

A neural network inversion control method for a flexible manipulator system, comprising: establishing a dynamic model of a mechanical-flexible manipulator servo system and performing an equivalent transformation, initializing the system state, sampling time and control parameters; combining sliding mode control and inversion Introduce virtual control variables in each design step, and finally derive the adaptive controller input; at the same time, use the approximation characteristics of neural networks to avoid the complexity explosion and the uncertainty of model parameters caused by the inversion method. Approximation; calculation of control system tracking error, integral sliding surface, error variable and differential. The invention provides a neural network inversion sliding mode control method capable of effectively improving the position tracking control performance of a flexible manipulator servo system, and realizes stable and fast tracking of the system.

Description

technical field [0001] The invention relates to a neural network control method for a flexible manipulator system, in particular to a neural network inversion control method for a flexible manipulator system with an uncertain model. Background technique [0002] The servo system of the manipulator has been widely used in high-performance systems such as robots and aviation vehicles. How to realize the fast and precise control of the servo system of the manipulator has become a hot issue. However, rigid manipulator systems often do not consider the flexibility of the joints, which often leads to reduced efficiency or even failure of the control system. In order to improve the tracking control performance, it is necessary to consider the flexible manipulator model. The flexible mechanical arm is to add the spring stiffness coefficient between the joints. Therefore, a more complex structural motion equation is introduced into the system model, which makes the control more diff...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B13/04
Inventor 陈强施琳琳
Owner GUANGZHOU ETON ELECTROMECHANICAL
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