Inverse kinematics learning for redundant robot manipulators with blending of support vector regression machines

Author(s):  
Jie Chen ◽  
Henry Y K Lau
2017 ◽  
Vol 50 (1) ◽  
pp. 8208-8213 ◽  
Author(s):  
Joseph Jean-Baptiste Mvogo Ahanda ◽  
Jean Bosco Mbede ◽  
Achille Melingui ◽  
Bernard Essimbi Zobo ◽  
Othman Lakhal ◽  
...  

Robotica ◽  
2017 ◽  
Vol 36 (4) ◽  
pp. 516-534 ◽  
Author(s):  
Joseph Jean-Baptiste Mvogo Ahanda ◽  
Jean Bosco Mbede ◽  
Achille Melingui ◽  
Bernard Essimbi

SUMMARYThis study derives a robust adaptive control of electrically driven robot manipulators using a support vector regression (SVR)-based command filtered adaptive backstepping approach. The robot system is supposed to be subject to model uncertainties, neglected dynamics, and external disturbances. The command filtered backstepping algorithm is extended to the case of the robot manipulators. A robust term is added to the common adaptive SVR algorithm, to mitigate the effects of the SVR approximation error in the path tracking performance. The stability analysis of the closed loop system using the Lyapunov theory permits to highlight adaptation laws and to prove that all the signals in the closed loop system are bounded. Simulations show the effectiveness of the proposed control strategy.


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