Robot manipulator contact force control application of fuzzy-neural network

2002 ◽  
Author(s):  
K. Kiguchi ◽  
T. Fukuda
Keyword(s):  
Neural Network ◽  
Contact Force ◽  
Force Control ◽  
Fuzzy Neural Network ◽  
Robot Manipulator ◽  
Fuzzy Neural ◽  
Control Application ◽  
Contact Force Control

scholarly journals Neural Network Based Contact Force Control Algorithm for Walking Robots

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 287
Author(s):  
Byeongjin Kim ◽  
Soohyun Kim
Keyword(s):  
Neural Network ◽  
Contact Force ◽  
Force Control ◽  
Control Algorithm ◽  
Position Control ◽  
Linear Quadratic Regulator ◽  
Walking Robots ◽  
Test Model ◽  
Linear Quadratic ◽  
Contact Force Control

Walking algorithms using push-off improve moving efficiency and disturbance rejection performance. However, the algorithm based on classical contact force control requires an exact model or a Force/Torque sensor. This paper proposes a novel contact force control algorithm based on neural networks. The proposed model is adapted to a linear quadratic regulator for position control and balance. The results demonstrate that this neural network-based model can accurately generate force and effectively reduce errors without requiring a sensor. The effectiveness of the algorithm is assessed with the realistic test model. Compared to the Jacobian-based calculation, our algorithm significantly improves the accuracy of the force control. One step simulation was used to analyze the robustness of the algorithm. In summary, this walking control algorithm generates a push-off force with precision and enables it to reject disturbance rapidly.

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.

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