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Robot arm system preset performance control method based on neural network

A control method and neural network technology, which are applied in the field of preset performance control of a robotic arm system, can solve problems such as difficulty in simultaneously ensuring the steady-state performance and transient performance of the system, the unstable operation of the system, and the deterioration of the system's motion performance.

Active Publication Date: 2020-02-04
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0003] There are often uncertain parts of the system in the servo system of the manipulator. If the controller is designed by ignoring the influence of these uncertain parts on the system, it may lead to poor motion performance of the system or even unstable operation of the system.
Algorithms such as PID control and adaptive control are often difficult to guarantee the steady-state performance and transient performance of the system at the same time, and it is necessary to repeatedly adjust the controller parameters to improve system performance

Method used

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  • Robot arm system preset performance control method based on neural network
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  • Robot arm system preset performance control method based on neural network

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Embodiment Construction

[0120] The present invention will be further described below in conjunction with the accompanying drawings.

[0121] refer to Figure 1-Figure 7 , a method for controlling preset performance of a manipulator system based on a neural network, comprising the following steps:

[0122] Step 1, establish the servo system model of the manipulator;

[0123] 1.1, the servo system model of the manipulator is expressed in the following form

[0124]

[0125] in, and is the uncertain item of the system model, d 1 , d 2 is the external interference signal, q is the angular position of the mechanical arm joint, the angular position of the θ motor, K is the joint elastic coefficient, I, J are the inertia coefficients of the mechanical arm and the motor, M, g, L are the mass and gravity of the mechanical arm Acceleration and the length of the manipulator, τ is the control torque of the manipulator;

[0126] 1.2, design barrier Lyapunov function

[0127]

[0128] Among them, t...

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Abstract

The invention relates to a robot arm system preset performance control method based on a neural network. The robot arm system preset performance control method based on the neural network comprises the following steps of: S1, establishing a mathematical model of a robot arm servo system, and designing a tangential barrier Lyapunov function; S2, designing a preset performance adaptive controller bycombining the tangential barrier Lyapunov function with an inversion method; and S3, carrying out stability analysis. According to the robot arm system preset performance control method based on theneural network, by setting parameter values of a constraint boundary function, steady-state performance and transient-state performance of the system can be ensured. Moreover, the neural network is utilized to approach an uncertain part of the model and a derivative of a virtual control quantity, so that design of the controller is effectively simplified, robustness of the system is improved to acertain degree, and the robot arm servo system can implement accurate and rapid tracking control.

Description

technical field [0001] The invention relates to a control method for preset performance of a manipulator system based on a neural network, in particular to an adaptive control method for a manipulator servo system whose system includes model uncertain items and external disturbances. Background technique [0002] The servo system of the manipulator has been widely used in high-tech fields such as robotics and medical treatment. Improving the steady-state performance and transient performance of the manipulator is of great significance and has become a research hotspot for scholars at home and abroad. Aiming at how to effectively improve the motion performance of the system, a variety of control methods have been proposed at home and abroad, including PID control, adaptive control, sliding mode control, neural network control, backstepping control, transient control, etc. Among them, the backstepping control has a simple algorithm, and can decompose the high-order system into...

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Application Information

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IPC IPC(8): G05B13/04B25J9/16
CPCG05B13/048B25J9/1656
Inventor 陈强丁科新徐栋南余荣
Owner ZHEJIANG UNIV OF TECH
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