This paper presents several neural network based control strategies for the trajectory control of robot manipulators. The neural networks learn the inverse dynamics of a robotic manipulator without any a priori knowledge of the manipulator inertial parameters or equation of dynamics. Compared are; a delta rule type that does not learn on line, the HSA which is similar but has a small stack of previous input output pairs that are used to train the network on-line, and the CMAC type that also learns on-line. Training strategies and difficulties with on-line training are discussed. Simulation of a two degree of freedom serial link manipulator allows comparison of the effectiveness of the algorithms. Results show various levels of performance.

Fourth International Conference on Control and Automation
Department of Systems and Computer Engineering

Showalter, I. (I.), & Schwartz, H.M. (2003). Comparison of adaptive neural network controllers of a non-linear robotic manipulator. In International Conference on Control and Automation (pp. 143–147).