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基于改进遗传算法的磨煤机模糊控制系统

Ball Mill Fuzzy Control System Based on Improved Genetic Algorithms

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【作者】 史冬琳冯玉昌戴春喜冯锁丽

【Author】 SHI Dong-lin, FENG Yu-chang, DAI Chun-xi, FENG Suo-li ( Institute of Automation, Northeast China Institute of Electric Power Engineering (University), Jilin 132012, China; Institute of Automation, North China Electric Power University, Baoding 071003, China; 3. Huaneng Hegang Power Plant, Hegang 154101, China)

【机构】 东北电力学院(大学)自动化学院华北电力大学自动化学院华能鹤岗发电厂华能鹤岗发电厂 吉林吉林132012河北保定071003黑龙江鹤岗154101黑龙江鹤岗154101

【摘要】 针对火电厂制粉系统中的钢球磨煤机由于具有纯滞后、大惯性、数学模型难以建立等特点采用数值型模糊控制器,使用伪并行遗传算法对其调整因子、量化殷子和比例因子进行优化,提出了一种适用于多变量对象的适应度函数,同时对交叉和变异算子进行分析和改进,仿真实验结果表明经过优化的模糊控制器具有较好的鲁棒性和抗干扰性。

【Abstract】 The ball mill Pulverizing system of power plant is difficult to model mathematically due to time delays and large time constants, so the process is hard to control. Fuzzy control due to its mechanism is fit to control the process whose exact mathematical model is hard to build. However in a fuzzy control system, the performance of fuzz)’ controller has a large effect on its control performance, which relies on how to adjust the fuzzy rules. To overcome the difficulties of selection and optimization of fuzzy control rules, a kind of numerical fuzzy controller is designed based on Pseudo-Parallel Genetic Algorithms (PPGA), and an adaptive function fit for multi-variable process is presented and the crossover and mutation operators are analysed and modified. The simulation experiments show good stability and robustness of the optimized fuzzy controller.

  • 【文献出处】 控制工程 ,Control Engineering of China , 编辑部邮箱 ,2005年S1期
  • 【分类号】TP273.5
  • 【被引频次】2
  • 【下载频次】131
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