Bounded decoupling control for flexible‐joint robot manipulators with state estimation

Cheng, Xin; Liu, Huashan

doi:10.1049/iet-cta.2019.1007

Cited by 11 publications

(2 citation statements)

References 42 publications

(55 reference statements)

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“…With the development of space technology, the research of space robot technology has been widely valued by scholars all over the world [1][2][3]. However, most of the research is based on the dynamics research and control algorithm design of space rigid body manipulator, that is, the space base, manipulator and joint are regarded as multi rigid body dynamic system, ignoring the influence of joint flexibility [4][5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network vibration suppression control of flexible joints space manipulator based on H∞ theory

You¹,

Zhang²,

Shen³

et al. 2023

J. vibroeng.

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Considering the control problems caused by uncertainties such as inaccurate modeling, external disturbance and joint flexibility, a neural network control method based on H∞ is proposed. By establishing the dynamic model of the free-floating space robot with flexible joints, according to its dynamic characteristics, it is split into a slow subsystem model representing the rigid characteristics and a fast subsystem model representing the flexible characteristics. Based on the H∞ robust control theory, a robust controller based on neural network is designed to realize the decoupling control of the rigid dynamic model, The designed weight adaptive learning rate can ensure the online and real-time adjustment of parameters. Based on Lyapunov theory, it is proved that the designed controller can ensure that the L2 gain of the system is less than the given index. A feedback controller based on velocity differential is designed to compensate the angle error caused by joint flexibility. The experimental simulation results verify that the proposed control method is effective and has good engineering application value.

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Section: Introductionmentioning

confidence: 99%

Adaptive neural network vibration suppression control of flexible joints space manipulator based on H∞ theory

You¹,

Zhang²,

Shen³

et al. 2023

J. vibroeng.

View full text Add to dashboard Cite

show abstract

“…This will bring great difficulties to the design of its controller (Ajwad et al, 2018; Han et al, 2021; He et al, 2015; Hong et al, 2020; Liu et al, 2019). It has been noted (Baumann et al, 2018) that the radial basis function (RBF) neural network has good generalization ability, and can effectively improve the performance of the controller when the system has a large uncertainty (Baumann et al, 2018; Cheng and Liu, 2020; Gambuzza et al, 2017; Gao et al, 2018; Jin et al, 2021; Kvatinsky et al, 2012; Rani and Kumar, 2019; Wang et al, 2019). However, with the continuous advancement of technology, the robotic manipulator control is required to become more and more intelligent.…”

Section: Introductionmentioning

confidence: 99%

Design of the MVT RBF neural network robotic manipulator control system based on model block approximation

Xiaoliang

Liu

2022

Transactions of the Institute of Measurement and Control

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Due to the uncertain dynamic characteristics, the requirements for robotic manipulator control are increasingly complex. The traditional radial basis function (RBF) neural network has a good generalization ability, but its redundant and tedious training process cannot meet the “Intelligent” control requirement of robotic manipulator. This study designs a new valve-regulated memristive RBF neural network, which adopts the model block approximation control strategy to estimate the three coefficient matrices of the robotic manipulator and uses the memristor with voltage threshold (MVT) as an electronic synapse to provide connections between neurons for the neural network and store information. This study adopts the design idea of software hardening and replaces the updated neural network weight with the change of the memristance value in the MVT network (crossed array), which can effectively improve the control performance of the traditional RBF neural network and can also provide analytical data for the fault detection of the subsequent control system. A simulation analysis is conducted with a single-joint robotic manipulator as the control object, and the results verify the rationality and feasibility of the proposed control algorithm.

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Decoupling control for a multi-DOF pneumatic manipulator subject to imbalanced torques

Zhao,

Chen,

2024

Nonlinear Dyn

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Bounded decoupling control for flexible‐joint robot manipulators with state estimation

Cited by 11 publications

References 42 publications

Adaptive neural network vibration suppression control of flexible joints space manipulator based on H∞ theory

Adaptive neural network vibration suppression control of flexible joints space manipulator based on H∞ theory

Design of the MVT RBF neural network robotic manipulator control system based on model block approximation

Decoupling control for a multi-DOF pneumatic manipulator subject to imbalanced torques

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