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Motor servo system bilateral neural network friction compensation and limited time coordination control method

A dual-neural network and servo system technology, applied in the field of motor servo system dual-neural network friction compensation and limited time cooperative control, can solve the problem that the precise model of the friction link is difficult to determine

Active Publication Date: 2014-12-10
广州伊东科技有限公司
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Problems solved by technology

An accurate model of the friction link is difficult to determine because some parameters of the friction model vary nonlinearly

Method used

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  • Motor servo system bilateral neural network friction compensation and limited time coordination control method
  • Motor servo system bilateral neural network friction compensation and limited time coordination control method
  • Motor servo system bilateral neural network friction compensation and limited time coordination control method

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Embodiment Construction

[0065] Refer to attached Figure 1-5 , the dual neural network friction compensation and finite-time cooperative control method for the motor servo system, including the following steps:

[0066] Step 1, establish the motor servo system model shown in formula (1), initialize the system state and related control parameters;

[0067] m x · · = - K f x + u ( t ) - F - - - ( 1 )

[0068] Among them, m is the effective mass; x is the state variable, indicating the position of the motor rotor; u(t) is the control signal, indicating the voltage changing with time; K f is the damping coefficient, which is a constant value parameter; F is the unknown nonlinear friction force. ...

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Abstract

Disclosed is a motor servo system bilateral neural network friction compensation and limited time coordination control method. The motor servo system bilateral neural network friction compensation and limited time coordination control method includes: building a motor servo system model, and initializing system states an related control parameters; building a Lugre model of nonlinear friction, and dividing the Lugre model of the nonlinear friction into a static portion and a dynamic portion; using a bilateral neural network to respectively estimate a static portion and a dynamic portion of the nonlinear friction, and designing a weight updating rule; according to a system equation, designing a limited time synergy controller, eliminating a buffeting problem in sliding mode control, and guaranteeing that the system states can rapidly and stably converge to a null point.

Description

technical field [0001] The invention relates to a dual neural network friction compensation and finite time cooperative control method for a motor servo system, in particular to a dual neural network compensation and control method for a motor servo system with model uncertainty and nonlinear friction. Background technique [0002] In a motor servo system, friction is generated when there is relative motion between solid surfaces that are in contact with each other, especially when the motor is moving at low speeds. For the motor servo system, the friction link has become one of the main obstacles to improve the system performance, which makes the system respond to crawling, oscillation or steady-state error. In order to reduce the adverse effect brought by the frictional link in the motor servo system and improve the working performance of the system, it is necessary to use an appropriate compensation control method to compensate the frictional link. [0003] At present, i...

Claims

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Application Information

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IPC IPC(8): G05B13/04
Inventor 陈强翟双坡汤筱晴
Owner 广州伊东科技有限公司
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