以制粉系统作为研究对象,提出了一种混合自适应粒子群算法(HAPSO)优化模糊PID的方法对制粉系统进行仿真控制研究。通过适应度函数对比仿真实验来验证HAPSO算法的寻优性能,并且将HAPSO算法优化模糊PID与传统PID控制,模糊PID控制和高斯函数递减惯性权重粒子群算法(GDIWPSO)优化模糊PID进行对比分析实验。实验结果表明:本文提出的混合自适应粒子群算法可以有效提高算法在全局中的搜索能力,可以更快更准确地找到问题的全局最优解。HAPSO算法优化模糊PID的方法与PID控制和模糊PID控制相比,超调量分别降低53.78%和57.67%；调节时间分别减少61.17%和53.82%。

Taking the pulverizing system as the research object, a fuzzy PID optimized by hybrid adaptive particle swarm optimization algorithm (HAPSO) method is proposed to simulate and control the pulverizing system. The optimization performance of HAPSO algorithm is verified by the simulation experiment of fitness function contrast, and the comparison experiments are carried out between the fuzzy PID control optimized by HAPSO algorithm and traditional PID control, fuzzy PID control and the fuzzy PID control optimized by Gaussian function decreasing inertia weight particle swarm optimization algorithm (GDIWPSO). Experimental results show that the hybrid adaptive particle swarm optimization algorithm proposed in this paper can effectively improve the algorithm"s global search ability, and can find the global optimal solution of the problem faster and more accurately. Compared with the PID control and the fuzzy PID control, the overshoots of the fuzzy PID optimized by HAPSO algorithm control are reduced by 55.39% and 59.10% respectively, and the adjusting times are reduced by 61.17% and 53.82% respectively.

TP273? ??