Research Information System
2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV, United States Of America, 27 - 31 October 2003, vol.1, pp.694-698
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.