可变风量（VAV）系统因其节能能力而在实验室行业中越来越受欢迎。然而，VAV系统为在各种运行条件下实现稳定准确的性能提出了新的控制挑战。本文提出了一种前馈和反馈相结合的控制方法，目的是提高实验室暖通空调系统的性能、经济高效、易于实现和操作。前馈部件采用通用回归神经网络（GRNN）进行HVAC系统识别和控制，而反馈部件则提供控制信号，以抵消任何稳态误差- 状态错误。这是一篇由三部分组成的论文的第一部分，该论文比较了组合控制方法与传统的仅反馈和前馈控制器的性能。比较了在带有VAV系统的实验室中常见的控制序列。本文概述了不同的控制方法。建立了压力控制序列，并建立了仿真模型。然后给出了组合、仅前馈和常规反馈控制方法的仿真结果。结果清楚地表明，在大范围变化的运行条件和不同的阻尼器特性下，组合方法的性能优于反馈方法。 该组合方法稳定，消除了所有稳态误差。单位:双引证:研讨会，ASHRAE交易，1998年，第104卷，第2部分，多伦多

Variable-air-volume (VAV) systems are growing in popularity in the laboratory industry due to their ability to conserve energy. However, VAV systems pose new control challenges to achieve a stable and accurate performance over a wide range of operating conditions. A combined feedforward and feedback control approach is proposed in this paper with the objective to enhance performance, to be cost effective, and to be easy to implement and operate for laboratory HVAC systems. The feedforward component employs a general regression neural network (GRNN) for HVAC system identification and control, while the feedback component provides a control signal to offset any steady-state error.This is Part I of a three-part paper that compares the performance of the combined control approach with conventional feedback and feedforward only controllers. The comparison is made for the control sequences commonly found in a laboratory with a VAV system. A general overview of the different control approaches is presented in this paper. The control sequence for pressure is developed, and a simulation model is built. Simulated results are then presented for the combined, feedforward only, and conventional feedback control approaches. The results clearly indicate that the combined approach performs better than the feedback approach over widely varying operating conditions and different damper characteristics. The combined approach is stable and eliminates all steady-state error.Units: Dual