Title :
An application of SMC theory for experimental learning control of robotic manipulators
Author :
Yildiran, Ugur ; Kaynak, Okyay
Author_Institution :
Dept. of Electr. & Electron. Eng., Bogazici Univ., Istanbul, Turkey
Abstract :
Complexity of learning dynamics constitutes a prime difficulty in online neurocontrol schemes involving gradient computations in parameter update rules. This is because such complexities can make closed loop system sensitive to uncertainties. In this paper, we discuss a learning control approach, which is based on the sliding mode control (SMC) techniques instead of gradient computations. Due to properties of SMC, learning process becomes robust to uncertainties. In order to test the control scheme, we have chosen a robotic manipulator as the test bed. Experimental results show that the control approach achieves a good tracking performance.
Keywords :
closed loop systems; gradient methods; learning (artificial intelligence); manipulators; neurocontrollers; variable structure systems; closed loop system; gradient computations; learning control; learning dynamics; online neurocontrol schemes; robotic manipulator; sliding mode control; Artificial neural networks; Backpropagation algorithms; Closed loop systems; Control systems; Intelligent networks; Manipulator dynamics; Robot control; Sliding mode control; Testing; Uncertainty;
Conference_Titel :
Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on
Print_ISBN :
0-7803-7860-1
DOI :
10.1109/IROS.2003.1250710
