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。
The control of dilution hydraulic headbox total pressure is directly related to the paper＇s quality. However, the accuracy of tradi- tional PID turning is low, while the standard particle swarm optimization algorithm（PSO） could improve the accuracy but it had a disadvan- tage 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.
出处
《中国造纸》
CAS
北大核心
2015年第11期37-41,共5页
China Pulp & Paper
关键词
流浆箱总压
PID自整定
改进粒子群优化算法
headbox total pressure
PID self-turning
improved particle swarm optimization
