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Neural-network-based adaptive finite time command filtering backstepping control method

A finite time, neural network technology, applied in the field of adaptive finite time command filtering backstepping control

Active Publication Date: 2020-01-14
QINGDAO UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0008] The purpose of the present invention is to propose a neural network-based self-adaptive finite time command filtering backstepping control method to solve the joint position tracking control problem of the flexible joint manipulator system with uncertainty and input saturation

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  • Neural-network-based adaptive finite time command filtering backstepping control method
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  • Neural-network-based adaptive finite time command filtering backstepping control method

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Embodiment Construction

[0157] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0158] Such as figure 1 As shown, the present invention describes a neural network-based adaptive finite-time command filter backstepping control method to solve the problem of joint position tracking control of a flexible joint manipulator system with uncertainty and input saturation.

[0159] This control method comprises the steps:

[0160] The dynamic model defining the flexible joint manipulator is as follows:

[0161]

[0162] in, represent the joint position, velocity and angular velocity vectors respectively; H(q)∈R n×n is a symmetric positive definite inertia matrix; is the Coriolis centripetal matrix; G(q)∈R n is the gravity vector; F∈R n×n is the diagonal positive definite matrix of the damping friction coefficient; Respectively represent the joint position, velocity and angular velocity vector of the motor after passing ...

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Abstract

The invention discloses a neural-network-based adaptive finite time command filtering backstepping control method. The method comprises the steps: constructing a finite time command filter; constructing an adaptive updating law based on neural network approximation; constructing a finite time error compensation mechanism, and constructing a dynamic auxiliary system for input saturation and the like. With the disclosed method, a problem of calculation complexity of the traditional backstepping method is solved and the errors generated in the filtering process are further eliminated. In addition, in order to further improve the robustness of the system, the method adopts a neural network approximation technology to approximate an uncertain dynamic model in the system. Besides, a dynamic auxiliary system is designed to compensate input saturation by considering the input saturation problem encountered by the actuator in the actual application process, so that the method is more suitable for actual application and a joint position tracking error is converged to an origin neighborhood which is small enough within limited time.

Description

technical field [0001] The invention relates to a neural network-based self-adaptive finite time command filter backstep control method. Background technique [0002] The joint flexibility of the manipulator caused by the joint action of the harmonic reducer and the torque sensor has become a bottleneck restricting the high-quality control of the robot. Therefore, many effective control methods have been developed, such as sliding mode control, backstepping control, neural network control, fuzzy Adaptive control, etc., to solve the problem of joint flexibility of the manipulator caused by the above reasons. [0003] Flexible articulated manipulators usually work in complex environments, so model uncertainty inevitably arises. Although the controller designed by the sliding mode control method can effectively suppress the system uncertainty, it usually has chattering problems. [0004] In contrast, backstepping technology, as another classic method to deal with high-order n...

Claims

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 赵林王冬冬于金鹏蔡明洁
Owner QINGDAO UNIV
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