Abstract
An iterative learning control scheme is presented. It incorporates a version of the cerebellar model articulation controller (CMAC) memory for the torque sequence generation. A learning rule is constructed by utilizing a gradient descent algorithm, and a map which updates old data stored in a distributed form is defined. It is shown that the training factor should be less than two for error convergence in the case of high-gain feedback.
| Original language | English |
|---|---|
| Pages (from-to) | 2613-2618 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 3 |
| State | Published - 1989 |
| Externally published | Yes |
| Event | Proceedings of the 28th IEEE Conference on Decision and Control. Part 2 (of 3) - Tampa, FL, USA Duration: 13 Dec 1989 → 15 Dec 1989 |