Abstract Variable static pressure is the main strategy for air volume adjustment in variable air volume air-conditioning system and can offer huge energy savings compared with constant air volume systems. When parameter set-points reset with load, dynamic processes are excited. Some processes take a long time to reach a new stable state with large overshoots. Improved iterative learning control (ILC) algorithm is studied to ameliorate transition processes of the static pressure reset control loop. A mathematical model of the static pressure control loop is established, and a new desired trajectory is given to improve the system tracking performance. Simulation and experimental studies are performed on traditional Proportion Integration Differentiation (PID) control, Proportion Differentiation (PD) type ILC, fuzzy gain PD type ILC and variable speed-integral PID type ILC. Results show that the system has the smallest overshoot and the shortest adjustment time with the fuzzy gain PD type ILC. Meanwhile the system can be betterly stabilized near the set-point with smallest evaluation index and the desired trajectory can be tracked with better dynamic performance. Studies show that the dynamic process is smoothed by improved ILC and variable trajectory. It is of great significance to enhance the whole system stability.

中文翻译：

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基于改进迭代学习控制和可变轨迹的空调系统静压设定点复位平滑方案

摘要 变静压是变风量空调系统中风量调节的主要策略，与定风量系统相比具有巨大的节能效果。当参数设定点随负载复位时，会激发动态过程。有些过程需要很长时间才能达到具有大超调的新稳定状态。研究改进的迭代学习控制（ILC）算法以改善静压复位控制回路的过渡过程。建立静压控制回路的数学模型，给出新的期望轨迹以提高系统跟踪性能。对传统的比例积分微分 (PID) 控制、比例微分 (PD) 型 ILC、模糊增益 PD 型 ILC 和变速积分 PID 型 ILC。结果表明，采用模糊增益PD型ILC的系统超调量最小，调整时间最短。同时，系统能够以最小的评价指标更好地稳定在设定点附近，并且能够以更好的动态性能跟踪期望的轨迹。研究表明，改进的 ILC 和可变轨迹使动态过程变得平滑。对提高整个系统的稳定性具有重要意义。研究表明，改进的 ILC 和可变轨迹使动态过程变得平滑。对提高整个系统的稳定性具有重要意义。研究表明，改进的 ILC 和可变轨迹使动态过程变得平滑。对提高整个系统的稳定性具有重要意义。