Neural network learning controller for manipulators
The adaptive learning control of robotic systems is a very important and challenging research problem. The objective of this paper is to design a learning controller for robots based on the biological model of the cerebellum for voluntary movement. In general, two neural network blocks are required...
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| Veröffentlicht in: | Neural networks 1988-01, Vol.1 (suppl.), p.356-356 |
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| container_end_page | 356 |
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| container_issue | suppl. |
| container_start_page | 356 |
| container_title | Neural networks |
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| creator | Pourboghrat, F Sayeh, M R |
| description | The adaptive learning control of robotic systems is a very important and challenging research problem. The objective of this paper is to design a learning controller for robots based on the biological model of the cerebellum for voluntary movement. In general, two neural network blocks are required in the design, to be in accord with the brain model. One network block is in the feedforward part of the controller which acquires the model of the inverse-dynamics of the robot. The other network block is in the feedback part of the controller which performs as an adaptive state feedback to compensate for the perturbations. |
| doi_str_mv | 10.1016/0893-6080(88)90384-X |
| format | Article |
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| ispartof | Neural networks, 1988-01, Vol.1 (suppl.), p.356-356 |
| issn | 0893-6080 |
| language | eng |
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| title | Neural network learning controller for manipulators |
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