Fuzzy-neural network compensator for Robot manipulator controlled by PD-like fuzzy system

2012 ◽  
Vol 8 (1) ◽  
pp. 35-44
Author(s):  
Turki Y. Abdalla ◽  
Basil H. Jasim
Keyword(s):  
Neural Network ◽  
Fuzzy System ◽  
Fuzzy Neural Network ◽  
Robot Manipulator ◽  
Fuzzy Neural ◽  
Neural Network Compensator

scholarly journals Fuzzy-neural network compensator for Robot manipulator controlled by PD-like fuzzy system

2012 ◽  
Vol 8 (8) ◽  
pp. 35-44
Author(s):  
Turki Abdalla ◽  
Basil Jasim
Keyword(s):  
Neural Network ◽  
Fuzzy System ◽  
Fuzzy Neural Network ◽  
Robot Manipulator ◽  
Fuzzy Neural ◽  
Error Space ◽  
Reference Trajectories ◽  
Neural Network Compensator ◽  
Input Error ◽  
Computed Torque

In this paper, high tracking performance control structure for rigid robot manipulator is proposed. PD-like Sugano type fuzzy system is used as a main controller, while fuzzy-neural network (FNN) is used as a compensator for uncertainties by minimizing suitable function. The output of FNN is added to the reference trajectories to modify input error space, so that the system robust to any change in system parameters. The proposed structure is simulated and compared with computed torque controller. The simulation study has showed the validity of our structure, also showed its superiority to computed torque controller.

Application of T-S Fuzzy Neural Network in the Assessment of River Ecosystem Health

2013 ◽  
Vol 726-731 ◽  
pp. 958-962 ◽  
Author(s):  
Zhen Chun Hao ◽  
Xiao Li Liu ◽  
Qin Ju
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy System ◽  
Ecosystem Health ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Fuzzy Model ◽  
River Ecosystem ◽  
Fuzzy Neural

Healthy river ecosystem has been acknowledged as the object of river management, which is crucial for the sustainable development of cities. Simple and practical evaluation methods with great precision are necessary for the evaluation of river ecosystem health. Fuzzy system has been widely used in evaluation and decision making for its simple reasoning and the adoption of experts knowledge. However, much artificial intervention decreases the precision. Neural network has a strong ability of self-leaning while it is not good at expressing rule-based knowledge. The T-S fuzzy neural network model combines the advantages of fuzzy system and neural network. In this paper, the T-S fuzzy neural network model was used to establish a river ecosystem health evaluation model. Results show that the combination of T-S fuzzy model and neural network eliminates the influences of subjective factors and improve the final precisions efficiently.

Design of fuzzy system-fuzzy neural network-backstepping control for complex robot system

2021 ◽  
Vol 546 ◽  
pp. 1230-1255
Author(s):  
Kunming Zheng ◽  
Qiuju Zhang ◽  
Youmin Hu ◽  
Bo Wu
Keyword(s):  
Neural Network ◽  
Fuzzy System ◽  
Fuzzy Neural Network ◽  
Backstepping Control ◽  
Robot System ◽  
Fuzzy Neural
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