Adaptive robust control for free-floating space robot with unknown uncertainty based on neural network

Zhang, Wenhui; Li, Hongsheng; Ye, Xiaoping; Huang, Jin; Huo, Mingying

doi:10.1177/1729881418811518

Cited by 7 publications

(3 citation statements)

References 20 publications

(46 reference statements)

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“…The results indicate that the CTC method is effective for manipulators with uncertainty if a compensation method is added. Wenhui et al (2018) proposed an online CTC for a space robot such that an NN controller approximates the unknown model. A conventional CTC which is combined with a time-delay estimation (TDE) technique to estimate the unmodelled dynamics and with an adaptive NN compensator to compensate TDE error is proposed for a rehabilitation exoskeleton in Han et al (2020).…”

Section: Introductionmentioning

confidence: 99%

NARMA-L2–based online computed torque control for robotic manipulators

Şen

Günel

2023

Transactions of the Institute of Measurement and Control

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Computed torque is an effective method for control of robotic manipulators. In this paper, a novel approach is proposed for computed torque control method, and also, it is integrated with online least squares support vector regression algorithm. The motive is to improve control performance and to build an adaptive architecture where no prior information on system model is required; the model is obtained continuously using a machine learning technique. There are two main contributions of the proposed work: the first one is the formulation of the computed torque control law using the nonlinear autoregressive moving average-L2 control methodology and the second contribution is the implementation of online least squares support vector regression method in estimating the dynamical model of the system and calculating the control law. The performance of the proposed method has been evaluated by simulations on a two-link robotic arm. The robustness of the method has also been evaluated for cases with disturbance and model uncertainty. Comparison with an existing computed torque control approach in the literature demonstrates the superiority of the proposed approach. Thus, it can be concluded that the method can be effectively used for first-order and second-order nonlinear systems without inherent instability.

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Section: Introductionmentioning

confidence: 99%

NARMA-L2–based online computed torque control for robotic manipulators

Şen

Günel

2023

Transactions of the Institute of Measurement and Control

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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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“…ANNs and other artificial intelligence methods, such as evolutionary computing algorithms, [7][8][9] have a wide variety of uses in robotics in areas such as computer vision 10,11 and path optimization [12][13][14] and navigation. 15,16 ANN emulates the human neural system using a structure of nodes, referred to as neurons, that are interconnected with weighted connections. 17,18 By adjusting this weight depending on the data, ANNs can provide the ability to classify or regress the input data to a solution with high precision.…”

Section: Introductionmentioning

confidence: 99%

Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator

Šegota

Anđelić

Mrzljak

et al. 2021

International Journal of Advanced Robotic Systems

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Inverse kinematic equations allow the determination of the joint angles necessary for the robotic manipulator to place a tool into a predefined position. Determining this equation is vital but a complex work. In this article, an artificial neural network, more specifically, a feed-forward type, multilayer perceptron (MLP), is trained, so that it could be used to calculate the inverse kinematics for a robotic manipulator. First, direct kinematics of a robotic manipulator are determined using Denavit–Hartenberg method and a dataset of 15,000 points is generated using the calculated homogenous transformation matrices. Following that, multiple MLPs are trained with 10,240 different hyperparameter combinations to find the best. Each trained MLP is evaluated using the R 2 and mean absolute error metrics and the architectures of the MLPs that achieved the best results are presented. Results show a successful regression for the first five joints (percentage error being less than 0.1%) but a comparatively poor regression for the final joint due to the configuration of the robotic manipulator.

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Adaptive robust control for free-floating space robot with unknown uncertainty based on neural network

Cited by 7 publications

References 20 publications

NARMA-L2–based online computed torque control for robotic manipulators

NARMA-L2–based online computed torque control for robotic manipulators

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

Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator

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