Deadzone compensation in motion control systems using neural networks

A compensation scheme is presented for general nonlinear actuator deadzones of unknown width. The compensator uses two neural networks (NN): one to estimate the unknown deadzone, and another to provide adaptive compensation in the feedforward path. The compensator NN has a special augmented form containing extra neurons whose activation functions provide a ´jump function basis set´ for approximating piecewise continuous functions. Closed-loop stability analysis for the deadzone compensator is provided, and yield tuning algorithms for the weights of the two NN. The technique provides a general procedure for using NN to determine the pre-inverse of an unknown right-invertible function.