CMAC based iterative learning control of robot manipulators
- Resource Type
- Authors
- Kwanghee Nam; Tae-Young Kuc
- Source
- Proceedings of the 28th IEEE Conference on Decision and Control.
- Subject
- Sequence
Cerebellar model articulation controller
Control theory
Computer science
Learning rule
Iterative learning control
Robot manipulator
Torque
Robot
Gradient descent
- Language
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. >