Dynamic neural networks based adaptive admittance control for redundant manipulators with model uncertainties

2019 ◽  
Vol 357 ◽  
pp. 271-281 ◽  
Author(s):  
Zhihao Xu ◽  
Shuai Li ◽  
Xuefeng Zhou ◽  
Taobo Cheng
Keyword(s):  
Neural Networks ◽  
Redundant Manipulators ◽  
Admittance Control ◽  
Model Uncertainties ◽  
Dynamic Neural Networks

Dynamic neural networks based kinematic control for redundant manipulators with model uncertainties

2019 ◽  
Vol 329 ◽  
pp. 255-266 ◽  
Author(s):  
Zhihao Xu ◽  
Shuai Li ◽  
Xuefeng Zhou ◽  
Wu Yan ◽  
Taobo Cheng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Redundant Manipulators ◽  
Model Uncertainties ◽  
Dynamic Neural Networks ◽  
Kinematic Control

scholarly journals Dynamic Neural Networks for Motion-Force Control of Redundant Manipulators: An Optimization Perspective

2021 ◽  
Vol 68 (2) ◽  
pp. 1525-1536 ◽  
Author(s):  
Zhihao Xu ◽  
Shuai Li ◽  
Xuefeng Zhou ◽  
Songbin Zhou ◽  
Taobo Cheng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Force Control ◽  
Redundant Manipulators ◽  
Dynamic Neural Networks

scholarly journals A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Choon Ki Ahn
Keyword(s):  
Neural Networks ◽  
Linear Matrix Inequality ◽  
Exponential Stability ◽  
External Disturbance ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Input Output ◽  
Lmi Approach ◽  
Dynamic Neural Networks ◽  
Numerical Examples

A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.

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