模糊神经网络PID在提升机恒减速系统中的研究
投稿时间:2019-09-29  修订日期:2019-11-18  点此下载全文
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作者单位邮编
王利栋 大同煤矿集团有限责任公司同大科技研究院 037003
王政 中国矿业大学 机电工程学院 221116
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
中文摘要:针对煤矿提升机恒减速系统现存在的问题,如超调量大,控制精度不高,响应时间不理想,提出了一种将模糊PID控制与BP神经网络相融合的提升机恒减速度优化算法。利用所构建的神经网络拓扑层结构和事先准备好的训练集数据,不断迭代优化隶属度函数和模糊规则的权值和阈值,以提高对提升机恒减速系统PID值的修整精度,最终实现对恒减速系统控制策略的优化,并在MATLAB中进行仿真,同时与其他算法进行对比。结果表明,模糊神经网络PID控制策略下响应更快,超调更符合行业要求。
中文关键词:提升机  恒减速  BP神经网络PID  模糊  隶属度  仿真
 
Research of fuzzy neural network PID in mine hoist constant deceleration system
Abstract:For coal mine hoister constant reduction system in the existing problems, such as big overshoot and control precision is not high, the response time is not ideal, this paper proposes a fuzzy PID control and the integration of the BP neural network in the elevator of constant deceleration optimization algorithm. Building by using neural network topology structure and prepared training set data, the iterative optimization of membership functions and fuzzy rules of weights and thresholds, in order to improve the precision finishing the PID value of constant deceleration system of hoist, finally realize the optimization of constant deceleration system control strategy, and carries on the simulation in MATLAB, at the same time, compared with other algorithms. The results show that the fuzzy neural network PID control strategy responds faster and overshoot is more in line with the industry requirements.
keywords:hoist  constant deceleration  BP neural network PID  fuzzy  membership  Simulation
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