- Ananthraman, S. and Garg, D.P., Training backpropagation and CMAC neural networks for control of a SCARA robot,
Eng. Appl. Artif. Intell. (UK), vol. 6 no. 2
pp. 105 - 15 [0952-1976(93)90026-T] .
(last updated on 2007/04/10)
The dynamic control of a robotic manipulator is accomplished by the computation and application of actuating torques required for the manipulator to follow desired trajectories. In order to address the problem of adaptive control in unknown environments, it is possible to utilize artificial neural networks to learn the characteristics of the system rather than having to prespecify an explicit system model. In the paper, two artificial-neural-network-based strategies are implemented for the accurate trajectory tracking by a SCARA-type IBM 7540 robot. The performance of a backpropagation-based neural controller is compared with that of one based on a scheme similar to Albus' cerebellar model articulation controller (CMAC) (Albus J.S. Trans. ASME J. Dynamic Syst. Measur. Control, p. 220-7 (1975))
adaptive control;backpropagation;manipulators;neural nets;position control;