Modal space neural network compensation control for Gough-Stewart robot with uncertain load

2021 ◽  
Vol 449 ◽  
pp. 245-257
Author(s):  
Xiaolin Dai ◽  
Shijie Song ◽  
Wenbo Xu ◽  
Zhangchao Huang ◽  
Dawei Gong
Keyword(s):  
Neural Network ◽  
Compensation Control ◽  
Modal Space

Robust adaptive neural network-based compensation control of a class of quadrotor aircrafts

2020 ◽  
Vol 357 (17) ◽  
pp. 12241-12263
Author(s):  
Xiao-Zheng Jin ◽  
Tao He ◽  
Xiao-Ming Wu ◽  
Hai Wang ◽  
Jing Chi
Keyword(s):  
Neural Network ◽  
Adaptive Neural Network ◽  
Compensation Control ◽  
Robust Adaptive

A rate-dependent KP modeling and direct compensation control technique for hysteresis in piezo-nanopositioning stages

2021 ◽  
pp. 1045389X2110235
Author(s):  
Rui Xu ◽  
Dapeng Tian ◽  
Miaolei Zhou
Keyword(s):  
Neural Network ◽  
Density Function ◽  
Control Technique ◽  
Dynamic Density ◽  
Compensation Control ◽  
Rate Dependent ◽  
The Neural Network ◽  
Reference Signals ◽  
Direct Compensation ◽  
Frequency Behavior

This paper first presents a rate-dependent Krasnosel’skii-Pokrovskii (RKP) model to capture the hysteresis of piezo-nanopositioning stages. The dynamic density function of the RKP model is obtained via neural network with frequency behavior input signal. Under the persistently exciting condition, the convergence of the neural network with Krasnosel’skii-Pokrovskii (KP) operators is proved rigorously. In order to address the hysteresis issue, a direct compensation control (DCC) approach with the KP compensation operator is proposed, where its dynamic density function is same as that of the RKP model. Some experiments with different reference signals are conducted to verify the effectiveness of the proposed modeling and DCC method on piezo-nanopositioning stages.

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