Adaptive Neural Network Based Virtual Decomposition Control for Robot Manipulator with Mixed Rigid/Flexible Joints

2021 ◽  
Author(s):  
Wei Xia ◽  
Huashan Liu
Keyword(s):  
Neural Network ◽  
Robot Manipulator ◽  
Flexible Joints ◽  
Adaptive Neural Network

scholarly journals Experimental Evaluation of a Sectorial Fuzzy Controller Plus Adaptive Neural Network Compensation Applied to a 2-DOF Robot Manipulator

2019 ◽  
Vol 52 (29) ◽  
pp. 233-238 ◽  
Author(s):  
Andres O. Pizarro-Lerma ◽  
Ramon Garcia-Hernandez ◽  
Victor Santibanez ◽  
Jorge Villalobos Chin
Keyword(s):  
Neural Network ◽  
Experimental Evaluation ◽  
Fuzzy Controller ◽  
Robot Manipulator ◽  
Adaptive Neural Network

Trajectory-Tracking-Based Adaptive Neural Network Sliding Mode Controller for Robot Manipulators

2020 ◽  
Vol 20 (3) ◽  
Author(s):  
Bin Ren ◽  
Yao Wang ◽  
Jiayu Chen
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Lyapunov Theory ◽  
Robot Dynamics ◽  
Sliding Mode Controller ◽  
Adaptive Neural Network ◽  
Proposed Model ◽  
The Impact

Abstract Unpredictable disturbances and chattering are the major challenges of the robot manipulator control. In recent years, trajectory-tracking-based controllers have been recognized by many researchers as the most promising method to understand robot dynamics with uncertainties and improve robot control. However, reliable trajectory-tracking-based controllers require high model precision and complexity. To develop an agile and straightforward method to mitigate the impact caused by uncertain disturbance and chattering, this study proposed an adaptive neural network sliding mode controller based on the super-twisting algorithm. The proposed model not only can minimize the tracking error but also improve the system robustness with a simpler structure. Moreover, the proposed controller has the following two distinctive features: (1) the weights of the radial basis function (RBF network) are designed to be adjusted in real-time and (2) the prior knowledge of the actual robot system is not required. The analytical model of the proposed controller was proved to be stable and ensured by the Lyapunov theory. To validate the proposed model, this study also conducted a comparative simulation on a two-link robot manipulator system with the conventional sliding mode controller and the model-based controller. The results suggest the proposed model improved the control accuracy and had fewer chattering.

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