Powiadomienia systemowe
- Sesja wygasła!
- Sesja wygasła!
Tytuł artykułu
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Aiming at the problems of delay and couple in the sintering temperature control system of lithium batteries, a fuzzy neural network controller that can solve complex nonlinear temperature control is designed in this paper. The influence of heating voltage, air inlet speed and air inlet volume on the control of temperature of lithium battery sintering is analyzed, and a fuzzy control system by using MATLAB toolbox is established. And on this basis, a fuzzy neural network controller is designed, and then a PID control system and a fuzzy neural network control system are established through SIMULINK. The simulation shows that the response time of the fuzzy neural network control system compared with the PID control system is shortened by 24s, the system stability adjustment time is shortened by 160s, and the maximum overshoot is reduced by 6.1%. The research results show that the fuzzy neural network control system can not only realize the adjustment of lithium battery sintering temperature control faster, but also has strong adaptability, fault tolerance and anti-interference ability.
Czasopismo
Rocznik
Tom
Strony
23--30
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr.
Twórcy
autor
- Guizhou Normal University, China
autor
- Guizhou Normal University, China
autor
- Guizhou Normal University, China
autor
- Guizhou Normal University, China
autor
- Guizhou Zhenhua New Material Co., Ltd., China
autor
- Guizhou Normal University, China
autor
- Guizhou Normal University, China
autor
- Guizhou Normal University, China
Bibliografia
- [1] Li, H.S., Miao, Q. & Zhou, R.M. (2014). Research on control algorithm of ceramic kiln temperature control system. Journal of Wuhan University of Technology. 36(10), 135-139. DOI: 10.3963/j.issn.1671-4431.2014.10.024.
- [2] Zhu, D.Q. & Jiang, K.R. (2009). Design and simulation of fuzzy neural network controller for drying process. Journal of System Simulation. 21(15), 4768-4771. DOI: 10.16182/j.cnki.joss.2009.15.079.
- [3] Zhang, Z.M., Zhang, J.Y. & Feng, X.G. (2019). Design of hot blast stove temperature control system based on RBF neural network tuning. Journal of Hebei University of Science and Technology. 40(06), 503-511. DOI: 10.7535/hbkd.2019yx06007.
- [4] Bai, G.Z. & Yu, J.H. (2016). PID parameter self-tuning based on improved fuzzy neural network. Computer Application Research. 33(11), 3358-3363+3368. DOI: 10.3969/j.issn.1001-3695.2016.11.035.
- [5] Li, C.L. & Huang, C.Z. (2010). Design of temperature control system based on fuzzy neural network. Microcomputer Information. 26(07), 75-76+98. DOI: 10.3969/j.issn.2095-6835.2010.07.031.
- [6] Li, G.Q., Tong, S.H. & Lu, L.X. (2013). Analysis of the temperature field in a continuous sintering furnace for solar cells. Computer Simulation. 30(01), 188-192+218. DOI: 10.3969/j.issn.1006-9348.2013.01.043.
- [7] Huang, B., Xie, G.J., Liang, W.S. & Zhang, J.W. (2018). Application of heating furnace temperature control system based on hybrid fuzzy PID. Electric Drive. 48(02), 43-46. DOI: 10.19457/j.1001-2095.20180208.
- [8] Zhou, G.L., Peng, Y.F. & Dong, H.S. (2007). Design of adaptive fuzzy PID controller based on T-S model. Industrial Instrumentation and Automation. 2, 22-25. DOI: CNKI: SUN: GYZD.0.2007-02-005.
- [9] Tan, M., Cheng, C.H. & Lu, C. (2006). Research on the temperature control system of vacuum sintering furnace based on neural network. Measurement and Control Technology. 2, 31-32+53. DOI: 10.19708/j.ckjs.2006.02.010.
- [10] Wang, J.P. & Ku, M.S. (2010). Application of FNN on atmospheric heating furnace of distillation unit. Computer Measurement and Control. 18(11), 2649-2651. DOI: 10.16526/j.cnki.11-4762/tp.2010.11.003.
- [11] Chen, B.F., Yin, P.L. & Ma, L. (2010). Research on temperature control system based on fuzzy neural network. Computer and Digital Engineering. 38(07), 54-57. DOI: 10.3969/j.issn.1672-9722.2010.07.016.
- [12] Li, M.H. & Li, Z.Q. (2012). Research on Decoupling Control Strategy of Injection Molding Machine Barrel Temperature Based on Neural Network. Ceramics. 4, 17-19. DOI: 10.19397/j.cnki.ceramics.2012.04.004.
- [13] Hu, Y.N. & Ma, W.M. (2017). Research and application of paper quantitative moisture control strategy based on FNN decoupling. China Paper. 36(07), 48-53. DOI: 10.11980/j.issn.0254-508X.2017.07.009.
- [14] Zhang, L., Zhang, J.C., Han, H.G. & Qiao, J.F. (2020). Process control of biochemical phosphorus removal in sewage treatment based on fuzzy neural network. CIESC Journal. 71(03). 1217-1225. DOI: 10.11949/0438-1157.20191514.
- [15] Tao, X.M., Luo, L. & Liu, Z.G. (2015). Research and simulation of injection molding machine barrel temperature control algorithm based on segmented PID. Plastics. 44(03), 68-70. DOI: CNKI: SUN: SULA.0.2015-03-022.
- [16] Zhou, P.Q. (2016). Research on synchronization control technology of double winches based on fuzzy neural network. Machine Design and Manufacture. 9, 64-68. DOI: 10.19356/j.cnki.1001-3997.2016.09.017.
- [17] Li, J.J., Xu, Y., Zhang, G., Wei, Z.Y. & Zhang, Y.B. (2015). Irrigation controller design based on BP neural network prediction and fuzzy control. Machine Design and Research. 31(05), 150-154. DOI: 10.13952/j.cnki.jofmdr. 2015.0207.
- [18] Luo, C.N., Hao, R.K. & Yang, W. (2018). Boiler temperature control simulation in industrial production process. Computer simulation. 35(09), 358-362. DOI: 10.3969/j.issn.1006-9348.2018.09.074.
- [19] Zhao, J. & Xu, H. (2016). Computer simulation study on segmental control of barrel temperature of injection molding machine. Synthetic Resin and Plastics. 33(05), 61-63. DOI: 10.3969/j.issn.1002-1396.2016.05.018.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8fdccdec-65b7-45ae-8e3b-aaf8058d2c0b