Internal model control using neural networks

We propose a design procedure of neural internal model control systems for processes with delay. We assume that a stable discrete-time neural model of the process is available. We show that the design of a model reference controller for internal model control necessitates only the training of the inverse of the model deprived from its delay, provided this inverse exists and is stable. As the robustness properties intrinsic to internal model control systems are only obtained if the inverse model is exact, it is also shown how to limit the effects of a possible inaccuracy of the inverse model due to its training. Computer simulations illustrate the proposed design procedure