Static pressure set-point reset smoothing scheme based-on improved iterative learning control and variable trajectory for air-conditioning system. (July 2020)

• Record Type: Journal Article
• Authors: Yan, Xiuying; Guo, Pujing; Zhang, Shaoxun; Yan, Xiulian; Liu, Cong
• Is Part Of: Journal of process control. Volume 91(2020)
• Page Start: 63
• Page End: 71
• Publication Date: 2020-07
• Subjects: Fuzzy gain -- Iterative learning control -- Desired trajectory -- Variable static pressure; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.03.016
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A duct static pressure control loop is taken as the research object. PID and PD-type, fuzzy-gain PD and variable speed-integral PID-type ILC is studied. Tracking performance is improved by ILC algorithm through experiment. Fuzzy gain PD-type ILC algorithm is better than that with other methods. Desired static pressure trajectory can be tracked with better dynamic performance. 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.