High accuracy adaptive motion control for a robotic manipulator with model uncertainties based on multilayer neural network

2021 ◽  
Author(s):  
Jian Hu ◽  
Pengfei Wang ◽  
Chenchen Xu ◽  
Haibo Zhou ◽  
Jianyong Yao
Keyword(s):  
Neural Network ◽  
Motion Control ◽  
Robotic Manipulator ◽  
High Accuracy ◽  
Model Uncertainties ◽  
Adaptive Motion

scholarly journals High-accuracy motion control of a motor servo system with dead-zone based on a single hidden layer neural network

2020 ◽  
Vol 30 (1) ◽  
pp. 27-44
Author(s):  
Jian Hu ◽  
Shupeng Cao ◽  
Chenchen Xu ◽  
Jianyong Yao ◽  
Zhiwei Xie
Keyword(s):  
Neural Network ◽  
Motion Control ◽  
Dead Zone ◽  
Servo System ◽  
High Accuracy ◽  
Hidden Layer

High-accuracy robust adaptive motion control of a torque-controlled motor servo system with friction compensation based on neural network

2018 ◽  
Vol 233 (7) ◽  
pp. 2318-2328 ◽  
Author(s):  
Jian Hu ◽  
Yuangang Wang ◽  
Lei Liu ◽  
Zhiwei Xie
Keyword(s):  
Neural Network ◽  
Motion Control ◽  
Servo System ◽  
High Accuracy ◽  
Robust Adaptive Control ◽  
Lyapunov Theory ◽  
Parametric Estimation ◽  
Friction Compensation ◽  
Nonlinear Friction ◽  
Robust Adaptive

In this paper, a high-accuracy motion control of a torque-controlled motor servo system with nonlinear friction compensation is presented. Friction always exists in the servo system and reduces its tracking accuracy. Thus, it is necessary to compensate for the friction effect. In this paper, a novel controller that combines robust adaptive control with friction compensation based on neural network observer is proposed. An improved LuGre friction model is applied into the friction compensation as it is known as a good model to express the nonlinear friction. A single hidden-layer network is utilized to observe the immeasurable friction state. Then, the robust adaptive controller is used to handle the parametric uncertainty, the parametric estimation error, friction compensation error, and other uncertainties. Lyapunov theory is utilized to analyze the stability of the closed-loop system. The experimental results demonstrate the effectiveness of the proposed algorithm.

Adaptive motion control using neural network approximations

Automatica ◽  
2002 ◽  
Vol 38 (2) ◽  
pp. 227-233 ◽  
Author(s):  
S.N. Huang ◽  
K.K. Tan ◽  
T.H. Lee
Keyword(s):  
Neural Network ◽  
Motion Control ◽  
Adaptive Motion

scholarly journals The Operational Space Formulation with Neural-Network Adaptive Motion Control

2008 ◽  
Vol 41 (2) ◽  
pp. 12775-12780 ◽  
Author(s):  
Dandy B. Soewandito ◽  
Denny Oetomo ◽  
Marcelo H. Ang
Keyword(s):  
Neural Network ◽  
Motion Control ◽  
Operational Space ◽  
Adaptive Motion ◽  
Space Formulation

Neural Network Based Perturbation Identification Approach for High Accuracy Tracking Control of Robotic Manipulators

2003 ◽  
Author(s):  
Jairo Moura ◽  
Martin Hosek
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Robotic Manipulator ◽  
High Accuracy ◽  
Direct Drive ◽  
Robot Tracking ◽  
Identification Approach ◽  
Manufacturing Applications ◽  
Settling Performance ◽  
Five Axis

This paper proposes an approach for high accuracy tracking control of a robotic manipulator in the presence of model perturbations. The proposed approach designs a neural network for estimation and compensation of the modeling errors, also referred to as perturbations. Experiments are carried out on a five-axis direct-drive robotic manipulator for automated pick-place operations in semiconductor manufacturing applications. It is shown that the proposed approach can substantially improve the robot tracking and settling performance of the original computed torque algorithm implemented.

Estimating Pigment Concentrations from Spectral Images Using an Encoder‐Decoder Neural Network

2020 ◽  
Vol 64 (3) ◽  
pp. 30502-1-30502-15
Author(s):  
Kensuke Fukumoto ◽  
Norimichi Tsumura ◽  
Roy Berns
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Absorption Coefficient ◽  
Spectral Data ◽  
High Accuracy ◽  
Pigment Concentration ◽  
Scattering Coefficient ◽  
A Value ◽  
Input And Output ◽  
Pigment Concentrations

Abstract A method is proposed to estimate the concentration of pigments mixed in a painting, using the encoder‐decoder model of neural networks. The model is trained to output a value that is the same as its input, and its middle output extracts a certain feature as compressed information about the input. In this instance, the input and output are spectral data of a painting. The model is trained with pigment concentration as the middle output. A dataset containing the scattering coefficient and absorption coefficient of each of 19 pigments was used. The Kubelka‐Munk theory was applied to the coefficients to obtain many patterns of synthetic spectral data, which were used for training. The proposed method was tested using spectral images of 33 paintings, which showed that the method estimates, with high accuracy, the concentrations that have a similar spectrum of the target pigments.

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

Sign in / Sign up

Export Citation Format

Share Document