Real-time control of variable air volume system based on a robust neural network assisted PI controller

We propose a novel neural network assisted proportional-plus-integral (PI) control strategy to improve the supply air pressure control performance of variable air volume (VAV) system. The neural network is trained on-line with a normalized training algorithm, which eliminates the requirement of a bounded regression signal to the system. To ensure the convergence of the training algorithm, an adaptive dead-zone scheme is employed. Stability of the proposed control scheme is guaranteed based on the conic sector theory. To demonstrate the applicability of the proposed method, real-time tests were carried out on a pilot VAV air-conditioning system and good experimental results were obtained.