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固液分离设备滤带自动纠偏系统设计 被引量:1

Design of Automatic Deviation Correction System for Filter Belt of Solid-liquid Separation Equipment
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摘要 为满足固液分离设备滤带及时、精准的纠偏要求,设计一种滤带自动纠偏系统。根据设备的结构,设计自动纠偏系统的纠偏方式和整体布局。设计基于免疫粒子群优化算法求解最佳参数的PID控制器,建立自动纠偏系统的数学模型,并对纠偏系统进行仿真分析。搭建试验平台模拟设备运行环境,对搭载该系统的设备样机进行了滤带纠偏测试、验证。结果表明:该系统纠偏及时、精准,满足设备运行要求。 To meet the requirements of timely and accurate correction of filter belt of solid-liquid separation equipment,an automatic correction system of filter belt is proposed.According to the equipment structure,the correction mode of the automatic deviation correction system and its overall layout are designed.A PID controller based on immune-particle swarm optimization algorithm is designed to solve the optimal parameters,and the mathematical model of automatic deviation correction system is established,on which simulation and analysis are conducted.A test platform is built to simulate the equipment operation environment,and the filter belt deviation correction test and verification are carried out on the equipment prototype equipped with the system.The results show that the deviation correction of the system is timely and accurate,and meets the requirements of equipment operation.
作者 宋斌 戚景观 吴文俊 SONG Bin;QI Jingguan;WU Wenjun(Jinxi Axle Company Limited,Taiyuan 030027,China)
出处 《机械制造与自动化》 2023年第1期166-169,共4页 Machine Building & Automation
关键词 固液分离设备 滤带 自动纠偏系统 免疫粒子群算法 solid-liquid separation equipment filter belt automatic deviation correction system immune particle swarm optimization
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  • 1Miller R K,Michel A N,Farrell J A.Quantizer effects on steady-state error specifications of digital feedback control systems[J].IEEE Trans on Automatic Control,1989,34(6):651-654.
  • 2De castro L N.Learning and optimization using the clonal selection principle[J].IEEE Transactions on Evolutionary Computation,2002,6(3):239-251.
  • 3Wang Q L,Wang C H,Gao X Z.A hybrid optimization algorithm based on clonal selection principle and particle swarm intelligence[C] //6th International Conference on Intelligent Systems Design and Applications.2006:975-979.
  • 4Gao X Z,Wang X,Ovaska S J.Fusion of clonal selection algorithm and differential evolution method in training cascade-correlation neural network[J].Neurocomputing,2009,72(10-12):2483-2490.
  • 5Kennedy J,Eberhart R.Particle swarm optimization[C] //Proceedings of the IEEE International Conference on Neural Networks.Perth,Australia,1995:1942-1948.
  • 6Shi Y,Eberhart R C.A modified particle swarm optimizer[C] //Proc of the IEEE Congress on Evolutionary Computation,Piscataway:NJ,1998:69-73.
  • 7Amaral J F M,Tanscheit R,Pacheco M A C.Tuning PID controllers through genetic algorithms[J].Advances in Fuzzy Systems and Evolutionary Computation,Part Ⅱ,World Scientific Engineering Society Press,2001:232-235.
  • 8廖常初.大中型PLC应用教程[M].北京:机械工业出版社,2004.
  • 9曾庭华,杨华,马斌,等.湿法烟气脱硫系统的安全性及优化[M].北京:中国电力出版社,2005.
  • 10贾长治,殷军辉,薛文星.MDADAMS虚拟样机从人门到精通[M].北京:机械工业出版社,2010.

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