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Neural network full order slip form control method for mechanical arm servo system

A servo system and control method technology, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve the problems that cannot be directly applied to the control of the mechanical arm

Active Publication Date: 2015-09-23
深圳市子庄智能科技有限公司
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Problems solved by technology

[0004] However, in most of the methods proposed above, the dynamic model parameters of the mechanical system must be known in advance
Therefore, when there are uncertain factors in the system, the method proposed above cannot be directly applied to the control of the manipulator

Method used

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  • Neural network full order slip form control method for mechanical arm servo system
  • Neural network full order slip form control method for mechanical arm servo system
  • Neural network full order slip form control method for mechanical arm servo system

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Embodiment Construction

[0078] The present invention will be further described below in conjunction with the accompanying drawings.

[0079] refer to Figure 1-Figure 9 , a neural network full-order sliding mode control method for a manipulator servo system, comprising the following steps:

[0080] Step 1, establish the dynamic model of the servo system of the manipulator, initialize the system state, sampling time and control parameters, the process is as follows:

[0081] 1.1 The expression form of the dynamic model of the manipulator servo system is:

[0082] M ( q ) q ·· + C ( q , q · ) q · + D q · + G ( q ) = ...

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Abstract

The invention discloses a neural network full order slip form control method for a mechanical arm servo system, which, aiming at the mechanical arm servo system containing a dynamic execution mechanism, is designed by utilizing the full-order slip form control method and combining with the neural network. The full order slip form is designed to guarantee the finite time convergence of the system and can eliminate buffeting and singular problems by avoiding the differential item in a practical control system. Besides, the neural network is used for approaching unknown non-linearity of the system and non-determinacy of internal-external disturbance. The neural network full order slip form control method for a mechanical arm servo system realizes provides a method to eliminate the buffeting and the singularity problem of the surface of the slip form, effectively complements the unknown linearity and the control method of the internal-external disturbance, and realizes fast and stable control of the system.

Description

technical field [0001] The invention relates to a neural network full-order sliding mode control method for a mechanical arm servo system, in particular to a control method for a mechanical arm servo system with a dynamic actuator and unknown dynamic parameters of the system. Background technique [0002] As a highly automated device, 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, unknown dynamic parameters and external disturbances widely exist in the servo system of the manipulator, which often lead to reduced efficiency or even failure of the control system. For the control problem of the servo system of the manipulator, there are many control methods, such as PID control, adaptive control, sliding mode control and so on. [0003] Sliding mode control is considered to be an effectiv...

Claims

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Application Information

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IPC IPC(8): G05B13/04
Inventor 陈强胡鑫
Owner 深圳市子庄智能科技有限公司
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