摘要
分数阶PID控制器继承了常规PID控制器的优点,并且具有更高的控制精度和更强的鲁棒性。针对常规PID控制器在纸浆浓度控制过程中存在的问题,设计了一种基于神经网络的分数阶PID控制器。用分数阶PID控制器代替常规PID控制器,并通过神经网络调节分数阶PID控制器的5个控制参数,实现一种参数自整定的PID控制器。仿真实验结果表明,神经网络分数阶PID控制器比常规PID控制器的控制精度高,对纸浆浓度的控制更稳定;采用神经网络分数阶PID控制器控制纸浆浓度是切实可行的,具有很好的推广应用前景。
Since fractional order PID inherits the advantages of traditional PID and has better control quality and higher robust, a fractional order PID controller based on artificial neural network was proposed and applied in pulp consistency control system. Using fractional order PID instead of the traditional PID, a self-tuning PID controller with five control parameters was realized by using parameter adjustment strate- gy of neural network. The simulation results showed that neural network fractional order PID controller had higher controlling accuracy and re- alized more stable control of pulp consistency than traditional PID controller. Control curve proved that the new controller was feasible and had popularizing value.
作者
单文娟
汤伟
王孟效
SHAN Wen-juan TANG Wei WANG Meng-xiao(College of Light Industry Science and Engineering, Shaanxi University of Science ＆ Technology, Xi＇an, Shaanxi Province, 710021 College of Electrical and Information Engineering, Shaanxi University of Science ＆ Technology, Xi＇an, Shaanxi Province, 710021 Shaanxi CIWE Process Automation Engineering Co. Ltd. , Xianyang, Shaanxi Province, 712099)
出处
《中国造纸学报》
CAS
CSCD
北大核心
2016年第4期44-48,共5页
Transactions of China Pulp and Paper
基金
国家国际科技合作项目(2010DFB43660)资助
关键词
纸浆浓度
分数阶PID控制器
神经网络
自整定
pulp consistency
fractional order PID controller
neural network
self-tuning
