摘要
空气源热泵系统是一个非线性强且大时滞的系统,采用常规的PID-PID串级控制难以达到对出水温度预期的控制效果。针对这一问题,建立了空气源热泵热水系统中的水流量与出水温度之间的数学模型。采用BP神经网络作为模型预测控制器及拟牛顿法进行目标误差函数数值优化,提出模型预测控制(MPC)算法与PID控制相结合的新型MPC-PID串级控制策略,并对空气源热泵热水系统进行跟踪性能和抗干扰性能测试。仿真结果表明,此控制策略提高了热泵系统的跟踪性能和抗干扰性能,还改善了系统强鲁棒性,其总体性能优于PID-PID串级控制系统。
The air source heat pump system is a system with strong nonlinearity and large time delay. It is difficult to achieve the expected control effect on the temperature of the effluent by using the conventional PID-PID cascade control. Aiming at this problem, a mathematical model of the water flow rate and water temperature in the air source heat pump hot water system is established. The BP neural network is used as the model predictive controller and the quasi-Newton method is used to optimize the target error function. A new MPC-PID cascade control strategy combining model predictive control(MPC) algorithm and PID control is proposed. The tracking performance and anti-interference performance of the air source heat pump hot water system are tested. The simulation results show that this control strategy improves the tracking performance and anti-interference performance of the heat pump system, and also improves the strong robustness of the system. Its overall performance is better than that of the PID-PID cascade control system.
作者
高龙
杨奕
任晓琳
于婧雅
韩青青
GAO Long;YANG Yi;REN Xiao-lin;YU Jing-ya;HAN Qing-qing(The College of Electrical Engineering,Nantong University,Nantong 226019,China)
出处
《控制工程》
CSCD
北大核心
2021年第9期1765-1772,共8页
Control Engineering of China
基金
国家自然科学基金面上项目(61973176)
江苏省研究生科研与实践创新计划项目(KYCX192060)
江苏省自然科学基金资助项目(BK20181457)
江苏省高校自然科学基金资助项目(18KJB120007)。
关键词
空气源热泵
模型预测控制
BP神经网络
串级控制
拟牛顿法
Air source heat pump
model predictive control
BP neural network
cascade control
quasi-Newton method
