DocumentCode :
3598649
Title :
Research on robust bionic learning algorithm in balance control for the robot
Author :
Shi Tao ; Ren Hongge ; Yin Rui ; Li Dongmei
Author_Institution :
Coll. of Electr. Eng., Hebei United Univ., Tangshan, China
fYear :
2015
Firstpage :
4387
Lastpage :
4390
Abstract :
In view of the nature unstable movement balance problems of the wheeled robot, the robust bionic autonomous learning algorithm is proposed as the wheeled robot learning mechanism based on the operant conditioning principle. The algorithm uses the characteristics of the robust control, which it can improve the system ability at suppressing interference and produce the optimum control behavior. Combining with the operant conditioning principle, the robot can simulate the biological operant conditioning mechanism, as well as the self-learning and self-adapting abilities through the interaction and learning and training with the unknown environment, and achieve the movement balance control of the wheeled robot. Finally, this paper uses this algorithm to make the simulation experiments in both cases of the absence of interference and interference respectively, and the compared results show that the robust bionic autonomous learning algorithm can reflect the better anti-interference ability, and make the robot obtain the skills about autonomous learning and balance control.
Keywords :
biomimetics; learning (artificial intelligence); mobile robots; optimal control; robot kinematics; robust control; self-adjusting systems; wheels; antiinterference ability; biological operant conditioning mechanism; conditioning principle; movement balance problems; optimum control behavior; robot balance control; robust bionic autonomous learning algorithm; robust control; self-adapting abilities; self-learning abilities; system ability; wheeled robot; Interference; Mobile robots; Robot kinematics; Robust control; Robustness; Wheels; Balance control; Bionic learning; Operant conditioning; Robot; Robust control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Print_ISBN :
978-1-4799-7016-2
Type :
conf
DOI :
10.1109/CCDC.2015.7162701
Filename :
7162701
Link To Document :
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