Abstract
This paper presents an optimal learning controller for uncertain robot systems which makes use of simple DNN(dynamic neural network) units to estimate uncertain parameters and learn the unknown desired optimal input. With the aid of a Lyapunov function, it is shown that all the error signals in the system are bounded and the robot trajectory converges to the desired one globally exponentially. The effectiveness of the proposed controller is shown by applying the controller to a planar robot manipulator.
| Original language | English |
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| Pages (from-to) | 1315-1319 |
| Number of pages | 5 |
| Journal | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
| Volume | 2 |
| State | Published - 1996 |
| Event | Proceedings of the 1996 IEEE International Conference on Systems, Man and Cybernetics. Part 4 (of 4) - Beijing, China Duration: 14 Oct 1996 → 17 Oct 1996 |