【摘要】 针对制粉系统提出了一种混合自适应粒子群算法(HAPSO)优化模糊PID的方法并进行仿真控制研究，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：提出的混合自适应粒子群算法可以有效提高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；与PID控制和模糊PID控制相比，HAPSO算法优化模糊PID方法的超调量分别降低62.01%和58.81%,调节时间分别减少51.45%和46.31%。更多还原

【Abstract】 A hybrid adaptive particle swarm optimization(HAPSO) method to optimize fuzzy PID for pulverizing system was proposed and the simulation control was studied. The optimization performance of HAPSO algorithm was verified by the simulation experiment of fitness function contrast, and the comparison experiments were carried out between the fuzzy PID control optimized by HAPSO algorithm with 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 HAPSO 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 are reduced by 62.01% and 58.81% respectively, and the adjusting time are reduced by 51.45% and 46.31% respectively.更多还原