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Mechanical arm system saturation compensation control method based on neural network dynamic surface sliding mode control

A neural network and control method technology, which is applied in the field of saturation compensation control of manipulator systems, can solve problems such as complexity explosion, inability of manipulator servo systems to effectively saturation compensation, and uncertainty of model parameters.

Active Publication Date: 2016-01-06
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0005] In order to overcome the shortcomings of the existing mechanical arm servo system, such as ineffective saturation compensation, model parameter uncertainty, and complexity explosion caused by the inversion method, the present invention provides a dynamic surface sliding mode control based on neural network. The saturation compensation control method of the manipulator system simplifies the design structure of the controller, realizes the position tracking control of the manipulator system with saturation input, and ensures the stable and fast tracking of the system

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  • Mechanical arm system saturation compensation control method based on neural network dynamic surface sliding mode control
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  • Mechanical arm system saturation compensation control method based on neural network dynamic surface sliding mode control

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Embodiment Construction

[0088] The present invention will be further described below in conjunction with the accompanying drawings.

[0089] refer to Figure 1-Figure 5 , a control method for saturation compensation of a manipulator system based on neural network dynamic surface sliding mode control, comprising the following steps:

[0090] 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:

[0091] 1.1 The expression form of the dynamic model of the manipulator servo system is

[0092] I q ·· + K ( q - θ ) + ...

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Abstract

A mechanical arm system saturation compensation control method based on neural network dynamic surface sliding mode control comprises the steps of: establishing a dynamic model of a mechanical arm servo system, and initializing system states, sampling time and control parameters; according to a differential mean value theorem, carrying out linearization processing on non-linear input saturation in the system, and deriving a mechanical arm servo system model with unknown saturation; and based on a dynamic surface sliding mode control, calculating a control system tracking error, a sliding mode surface and a differential. The invention provides the neural network dynamic surface sliding mode control method which is capable of effectively compensating unknown saturation, avoiding a complexity explosion problem caused by an inversion method and realizing stable and rapid tracking of the system.

Description

technical field [0001] The invention relates to a control method for manipulator system saturation compensation based on neural network dynamic surface sliding mode control, in particular to a control method for a manipulator servo system with input saturation constraints. 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. The trajectory tracking control system of the robot arm and the problem of the flexible robot arm have received more and more attention. However, the unknown saturated nonlinear link widely exists in the servo system of the manipulator, which often leads to a decrease in the efficiency of the control system or even failure. Therefore, the constraint of input saturation must be considered during the controller design process. For the control problem ...

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Application Information

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IPC IPC(8): G05B13/04
Inventor 陈强施琳琳
Owner ZHEJIANG UNIV OF TECH
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