Intelligent nonlinear observer design for a class of nonlinear discrete-time flexible joint robot

Khoygani, M. R. Rahimi; Ghasemi, Reza; Vali, Ahmad Reza

doi:10.1007/s11370-014-0162-x

Cited by 15 publications

(8 citation statements)

References 29 publications

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“…There are many consistent research findings that can be found. In , the high frequency motor position errors of flexible joint are shown by an artificial neural network observer. In actual robotic applications, many friction compensation controllers are proposed to improve the position or velocity tracking performance , and one can see that the high frequency control signals exist in the servo control system with friction compensation.…”

Section: Mathematical Simulationsmentioning

confidence: 99%

Backstepping Control for Flexible Joint with Friction Using Wavelet Neural Networks and L₂‐Gain Approach

Chu

Jia

2017

Asian Journal of Control

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In this study, a new backstepping control scheme is proposed to deal with the high accuracy flexible joint servo system's position control. Based on the introduction of non‐consecutive friction, the cascade dynamics equations of flexible joint are established. The macroscopic controller is designed using a backstepping design technique to suppress the flexibility and external disturbance based on the L2 property. To identify the non‐consecutive function, the wavelet neural networks (WNN) are utilized in the microscopic controller to compensate for nonlinear friction and uncertainties. The combined strategy of macro and micro controller can overcome the derivation explosion problem and avoid the joint acceleration measurement and upper bound forecast. Finally, stability analysis and mathematical simulations are presented to verify the effectiveness of this new controller.

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Section: Mathematical Simulationsmentioning

confidence: 99%

Backstepping Control for Flexible Joint with Friction Using Wavelet Neural Networks and L₂‐Gain Approach

Chu

Jia

2017

Asian Journal of Control

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“…In this paper, the universal approximation of neural networks is used as disturbance observer to observe the overall compound disturbance of system. And a robust adaptive controller is designed consequently to make input i y in system (3) and ( 1) , …”

Section: Problem Formulationmentioning

confidence: 99%

“…Due to these uncertainties system error becomes large, when a robot manipulator operates at high speed. It is a challenging problem in control field to find an effective control scheme to achieve accurate tracking of the desired motion (Khoygani et al, 2015;Singh et al, 2013;Han et al, 2015;Cuong et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Neural Network Disturbance Observer-Based Adaptive Tracking Control for Robot Manipulator

Huang¹,

Guo²,

Qu³

2017

IJCA

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“…One of the most efficient tools for designing a nonlinear observer system is them [18]. Using the fact that a vast range of nonlinear functions with any demanded degree of accuracy under specific conditions networks can estimate, lots of research has been conducted in the proposed observer [19]. Meanwhile, to classify patterns, forecasting, diagnosis, and approximation, multi-layer perceptron neural networks are used.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Observer-Based Controller Design for Nonlinear Type-1 Diabetes Model via Adaptive Neural Network Method

khoygani

Khoygani

Ghasemi

2021

IJRCS

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Diabetes is an increasing health problem all around the world, particularly Type 1 diabetes (T1D), people with T1D require precise glycemic control, due to a shortage of insulin production. This paper introduces a new adaptive neural observer-based controller for a class of nonlinear T1D systems. A solution is proposed to guarantees practical tracking of a desired glucose concentration by a new adaptive neural observer-based control strategy. One of the intelligence procedures is the network under online learning that the mentioned controller is learned by a back-propagation algorithm. This network is a significant class of feed-forward artificial neural networks that maps a set of inputs into a set of proper outputs. Guarantee stability of observer and controller by Lyapunov direct and training online are the merit of the method. Also, despite the presence of internal and external uncertainties, the multilayer perceptron neural observer-based controller is robust. The performance of the proposed method is hopeful based on the results.

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Intelligent nonlinear observer design for a class of nonlinear discrete-time flexible joint robot

Cited by 15 publications

References 29 publications

Backstepping Control for Flexible Joint with Friction Using Wavelet Neural Networks and L₂‐Gain Approach

Backstepping Control for Flexible Joint with Friction Using Wavelet Neural Networks and L₂‐Gain Approach

Neural Network Disturbance Observer-Based Adaptive Tracking Control for Robot Manipulator

Intelligent Observer-Based Controller Design for Nonlinear Type-1 Diabetes Model via Adaptive Neural Network Method

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Intelligent nonlinear observer design for a class of nonlinear discrete-time flexible joint robot

Cited by 15 publications

References 29 publications

Backstepping Control for Flexible Joint with Friction Using Wavelet Neural Networks and L2‐Gain Approach

Backstepping Control for Flexible Joint with Friction Using Wavelet Neural Networks and L2‐Gain Approach

Neural Network Disturbance Observer-Based Adaptive Tracking Control for Robot Manipulator

Intelligent Observer-Based Controller Design for Nonlinear Type-1 Diabetes Model via Adaptive Neural Network Method

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Backstepping Control for Flexible Joint with Friction Using Wavelet Neural Networks and L₂‐Gain Approach

Backstepping Control for Flexible Joint with Friction Using Wavelet Neural Networks and L₂‐Gain Approach