Real time adaptive control experiments with a multiple neural network based DCAL controller

The results of real time experiments are presented for a desired compensation adaptive law (DCAL) neural controller developed using orthonormal activation function-based neural networks (OAFNN). The task of the controller is to track the desired trajectory for a class of nonlinear systems. Multiple OAFNNs are employed in the neural adaptive controller for feed-forward compensation of unknown system dynamics. The feed-forward compensation is based on desired trajectory allowing flexibility of its off-line computation. Choice of multiple OAFNNs allows a reduction in overall network size reducing the computational requirements for real time implementation. The network weights are tuned online, in real time. The overall stability of the system and the neural networks is guaranteed using Lyapunov analysis. The effects of network parameters on system performance are evaluated and presented in this research. The superior learning capability of OAFNNs is demonstrated through experimental results