Mechanical-arm servo-system neural-network full-order sliding mode control method with dead-zone compensation

A technology of servo system and control method, applied in the direction of adaptive control, general control system, control/regulation system, etc.

Active Publication Date: 2015-12-23
扬州祥帆重工科技有限公司
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Problems solved by technology

[0005] In order to overcome the deficiency that the existing manipulator servo system cannot avoid the influence of the dead zone input on the system and the chattering problem of the sliding mode control, the present invention provides a neural network full-order sliding mode control of the manipulator servo system with dead zone compensation method, realizes effective compensation for unknown dead zone, improves chattering and singularity problems of the system, and ensures fast and stable convergence of the system

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  • Mechanical-arm servo-system neural-network full-order sliding mode control method with dead-zone compensation
  • Mechanical-arm servo-system neural-network full-order sliding mode control method with dead-zone compensation
  • Mechanical-arm servo-system neural-network full-order sliding mode control method with dead-zone compensation

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[0082] The present invention will be further described below in conjunction with the accompanying drawings.

[0083] Referring to Figure 1- Figure 4 , a neural network full-order sliding mode control method for a manipulator servo system with dead zone compensation, comprising the following steps:

[0084] 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:

[0085] 1.1 The dynamic model expression of the manipulator servo system is:

[0086] M H ( q ) q ·· + C H ( q , q · ) q · + D H q · ...

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Abstract

A mechanical-arm servo-system neural-network full-order sliding mode control method with dead-zone compensation is disclosed. Aiming at a mechanical arm servo system which contains a dynamic execution mechanism and is with unknown dead-zone input, a full-order sliding mode control method is used and a neural network is combined so as to design the mechanical-arm servo-system neural-network full-order sliding mode control method with the dead-zone compensation. A dead zone is converted into a linear time-varying system, and then the neural network is used to approach an unknown function so as to compensate an additional influence of a traditional unknown dead zone and an unknown parameter of the system. In addition, a full-order sliding mode surface is designed so as to guarantee rapid and stable convergence of the system; generation of a differential term is avoided in an actual control system so that buffeting is improved and a singular problem is solved. The invention provides the control method which can improve a buffeting problem of the sliding mode surface, solve the singular problem and can effectively compensate a system unknown dynamic parameter and unknown dead zone input so that rapid and stable control of the system is realized.

Description

technical field [0001] The invention relates to a neural network full-order sliding mode control method of a mechanical arm servo system with dead zone compensation, in particular to a control method of a mechanical arm servo system with unknown dead zone input 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, the unknown dead zone input widely exists in the servo system of the manipulator, which often leads to the reduction of the efficiency of the control system or even failure. 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 conside...

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Application Information

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IPC IPC(8): G05B13/04
Inventor 陈强胡鑫
Owner 扬州祥帆重工科技有限公司
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