Real-Time Control of Variable Air Volume System Based on a Robust Neural Network Assisted PI Controller

A neural network assisted proportional-plus-integral (PI) control strategy is proposed to improve the air pressure control performance of variable air volume (VAV) system. The neural network is trained online 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 are obtained.