Neural network based controllers for a single-degree-of-freedom robotic arm

Abstract

The properties of different neural network architectures in adaptive nonlinear robotic control are examined. For the comparison of architectures, a specific robotic problem was developed. This robotic system was controlled by three different neural-network-based architectures, and the controllers were analyzed and compared. It was found that significant improvements in control can be made by tailoring the neural network inputs and error structure. Also, temporal shifting of error information in the neural network backward error propagation can modify the spectral density of the controller function