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基于IPSO的PID参数自整定在流浆箱总压控制中的应用
Application of the PID Parameters Self-tuning Based on IPSO in Headbox Total Pressure Control
收稿日期：  
DOI：10.11980/j.issn.0254-508X.2015.11.008
关键词:  流浆箱总压  PID自整定  改进粒子群优化算法
Key Words:headbox total pressure  PID self-turning  improved particle swarm optimization
基金项目:
作者单位
陈帅帅 南京林业大学江苏省制浆造纸科学与技术重点实验室，江苏南京，210037 
赵倩梅 南京林业大学江苏省制浆造纸科学与技术重点实验室，江苏南京，210037 
熊智新* 南京林业大学江苏省制浆造纸科学与技术重点实验室，江苏南京，210037 
胡慕伊 南京林业大学江苏省制浆造纸科学与技术重点实验室，江苏南京，210037 
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摘要:稀释水水力式流浆箱的总压控制直接关系到纸张质量的好坏，而传统的PID整定方法精度较低，使用标准粒子群优化算法可以提高精度但是算法敛速度较慢。针对这些问题，采用改进的粒子群优化算法来自整定 PID参数，通过使用非线性递减惯性系数和动态加速因子策略来提高算法的寻优速度及精度。仿真结果表明，用改进的粒子群优化算法整定后的流浆箱总压控制PID有更好的响应速度和鲁棒性。
Abstract:The control of dilution hydraulic headbox total pressure is directly related to the paper’s quality. However, the accuracy of traditional PID turning is low, while the standard particle swarm optimization algorithm(PSO) could improve the accuracy but it had a disadvantage of slow convergence speed.Aiming at those problems, an improved particle swarm optimization algorithm(IPSO) was adopted to self-tune PID parameters in this paper. The speed and accuracy of optimization were improved by using the nonlinear decreasing inertia coefficient and dynamic acceleration factors. Simulation results showed that the headbox total pressure PID controller turned by IPSO algorithm had a better response speed and robustness.
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