目的 针对粒子群算法（PSO）整定大时滞PID控制器参数过程中搜索范围较大，搜索能力较差，甚至出现不收敛的问题，提出一种基于H∞理论的小范围搜索且带有目标性初始化粒子群的改进PSO算法（HOI-PSO）。方法 利用H∞理论确定PSO算法的初始搜索范围，融合信息熵对初始化粒子群进行评估、调整，从而获得分散性较高的初始种群。结果 Matlab仿真实验表明，HOI-PSO算法能够提高PSO算法的收敛速度，具有同大范围相似甚至更好的全局寻优能力；对于大时滞过程控制，闭环系统的控制性能得到很大改善。结论 HOI-PSO算法应用于长网造纸机定量回路的控制结果表明，采用信息熵PSO算法整定出的PID控制器参数对大时滞过程具有良好的控制效果，在实际生产中也具有一定的理论指导意义。

The work aims to propose a modified PSO algorithm based on H∞ theory with small search space and objectively initialized particle swarm (HOI-PSO) regarding the problem that during tuning PID controller parameters for large time delay processes, particle swarm optimization (PSO) with large search space will have poor search ability and even cannot converge. H∞ theory was used to determine the initial search band for PSO. Information entropy was integrated to evaluate and tune the initialized particle swarm in order to get the initial population of higher dispersion. Matlab simulation showed that, HOI-PSO algorithm could enhance the convergence speed of PSO algorithm and had the similar or even better global optimization capability as the scope of large range search; for large time delay process control, the control performance of the closed loop system was greatly improved. The results of the application of HOI-PSO algorithm in the control of the basis weight loop of the fourdrinier paper machine show that, the PID controller parameters tuned by the information entropy PSO algorithm have good control effect on the large time delay process and provide certain theoretical guiding significance in actual production.