Motion Control for Piezoelectric-Actuator-Based Surgical Device Using Neural Network and Extended State Observer

2020 ◽  
Vol 67 (1) ◽  
pp. 402-412 ◽  
Author(s):  
Jun Yik Lau ◽  
Wenyu Liang ◽  
Kok Kiong Tan
Keyword(s):  
Neural Network ◽  
Motion Control ◽  
Piezoelectric Actuator ◽  
State Observer ◽  
Extended State Observer ◽  
Extended State ◽  
Surgical Device

Fuzzy PID Motion Control Based on Extended State Observer for AUV

2019 ◽  
Author(s):  
Qingqing Yin ◽  
Yue Shen ◽  
Hongjia Li ◽  
Junhe Wan ◽  
Dianrui Wang ◽  
...  
Keyword(s):  
Motion Control ◽  
State Observer ◽  
Extended State Observer ◽  
Fuzzy Pid ◽  
Extended State

scholarly journals High-Speed High-Precision Motion Control of Robots Using Extended State Observer.

2000 ◽  
Vol 18 (2) ◽  
pp. 244-251 ◽  
Author(s):  
Bagus Mahawan ◽  
Zheng-Hua Luo ◽  
Jing-Qing Han ◽  
Shin-ichi Nakajima
Keyword(s):  
Motion Control ◽  
High Precision ◽  
High Speed ◽  
State Observer ◽  
Extended State Observer ◽  
Extended State ◽  
Precision Motion Control ◽  
Precision Motion

Adaptive neural impedance control with extended state observer for human–robot interactions by output feedback through tracking differentiator

2020 ◽  
Vol 234 (7) ◽  
pp. 820-833
Author(s):  
JianTao Yang ◽  
Cheng Peng
Keyword(s):  
Neural Network ◽  
Output Feedback ◽  
Impedance Control ◽  
State Observer ◽  
Lyapunov Theory ◽  
Extended State Observer ◽  
Compact Set ◽  
Extended State ◽  
Adaptive Neural Network ◽  
Tracking Differentiator

Although impedance control has huge application potential in human–robot cooperation, its engineering application is still quite limited, owing to the high nonlinearity of the human–robot dynamics and disturbances. This article presents a novel adaptive neural network controller with extended state observer for the human–robot interaction using output feedback. The adaptive neural network with extended state observer integrates the adaptive neural network and extended state observer to combine their advantages. The proposed algorithm can address the challenges encountered in human–machine systems, for example, slow convergence of neural networks, internal and external disturbances. Output feedback is realized using tracking differentiator to avoid the costly measurements of certain states. The errors of the closed-loop system are proven to converge to a small compact set containing 0 by Lyapunov theory. Simulations and experiments were conducted to verify the effectiveness of the proposed controller. Results show that the proposed strategy offers superior convergence and better tracking performance compared with the adaptive neural network. The proposed controller can be widely applied in various human–machine interactions to enhance productivity and efficiency.

scholarly journals RBF Neural Network Control for Linear Motor-Direct Drive Actuator Based on an Extended State Observer

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Zhi Liu ◽  
Tefang Chen
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Linear Motor ◽  
State Observer ◽  
Network Control ◽  
Extended State Observer ◽  
Direct Drive ◽  
Extended State ◽  
Hydraulic Power ◽  
Loop Control

Hydraulic power and other kinds of disturbance in a linear motor-direct drive actuator (LM-DDA) have a great impact on the performance of the system. A mathematical model of the LM-DDA system is established and a double-loop control system is presented. An extended state observer (ESO) with switched gain was utilized to estimate the influence of the hydraulic power and other load disturbances. Meanwhile, Radial Basis Function (RBF) neural network was utilized to optimize the parameters in this intelligent controller. The results of the dynamic tests demonstrate the performance with rapid response and improved accuracy could be attained by the proposed control scheme.

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