Multiple neural-network-based adaptive controller using orthonormal activation function neural networks

Abstract

A direct adaptive control scheme is developed using orthonormal activation function-based neural networks (OAFNN's) for trajectory tracking control of a class of nonlinear systems. Multiple OAFNN's are employed in these controllers for feedforward compensation of unknown system dynamics. Choice of multiple OAFNN's allows a reduction in overall network size reducing the computational requirements. The network weights are tuned on-line, in real time. The overall stability of the system and the neural networks is guaranteed using Lyapunov analysis. The developed neural controllers are evaluated experimentally and the experimental results are shown to support theoretical analysis. The effects of network parameters on system performance are experimentally evaluated and are presented in this research. The superior learning capability of OAFNN's is demonstrated through experimental results. The OAFNN's were able to model the true nature of the nonlinear system dynamics characteristics for a rolling-sliding contact as well as for stiction.