摘要
太阳能新风系统将太阳能技术与相变蓄热两种方法应用于供暖系统中,通过太阳的能量使得新风温度升高,实现可再生能源的节能目标。当前,新风温度控制系统的控制方法虽然容易实现,但其控制性能不佳,超调现象较为明显。对温控模型进行分析,利用粒子群优化(PSO)算法对温控模型进行优化整定,得到最优的3个比例积分微分(PID)控制参数,并提出一类基于粒子群参数整定后的模型参考自适应PID控制方式,对室内新风系统温度进行控制。经MATLAB仿真证实,该控制方法的调节时间更少、超调量更小,取得了良好的控制性能,能够很好地应用于实际生产当中。
The solar fresh air system applies two methods of solar energy technology and phase change heat storage to the heating system.Through the energy of the sun,the temperature of the fresh air is raised to achieve the energy saving goal of renewable energy.Although the current control method of fresh air temperature control system is easy to implement,its control performance is poor and the overshoot phenomenon is obvious.The temperature control model is analyzed and optimized by particle swarm optimization(PSO)algorithm,and three optimal proportional integral differential(PID)control parameters are obtained.A model reference adaptive PID control method based on particle swarm optimization was proposed to control the temperature of indoor fresh air system.MATLAB simulation proves that the control method has less adjusting time and overshoot,and has achieved good control performance,which can be well applied to actual production.
作者
王丽丽
辛玲
WANG Lili;XIN Ling(School of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)
出处
《自动化仪表》
CAS
2021年第12期51-55,共5页
Process Automation Instrumentation
基金
山东省自然科学基金资助项目(2015ZRB019FA)。
关键词
比例积分微分控制
模型参考
自适应控制
参数整定
粒子群优化
新风系统
超调量
Proportional integral differential(PID)control
Model reference
Adaptive control
Parameter setting
Particle swarm optimization(PSO)
Fresh air system
Overshoot amount
