Fuzzy self-adaptive PID control for fused deposition modeling 3D printer heating system

被引：0
作者：
Qu X.-T. [1 ]
Wang X.-X. [1 ]
Sun H.-C. [1 ]
Zhang K. [1 ]
Yan L.-W. [1 ]
Wang H.-Y. [1 ]
机构：
[1] College of Mechanical and Aerospace Engineering, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2020年 / 50卷 / 01期
关键词：
3D printing; Automatic control technology; Fuzzy PID; Simulation model; Temperature control;
D O I：
10.13229/j.cnki.jdxbgxb20190114
中图分类号：
学科分类号：
摘要：
In the process of FDM 3D printing, it is necessary to heat the printing nozzle and the heating bed of the printing platform until the required temperature of the printing material is reached. The heating process is time-consuming and energy-wasting due to the time lag and poor stability of the heating system. In order to solve the above problems, the paper adopts the fuzzy self-adaptive PID control method to control the heating process of the printing nozzle and the heating bed of the printing platform, and establishes the Matlab/Simulink simulation model of the control system. The simulation results show that the control effect of the fuzzy self-adaptive PID control method on the heating system of FDM 3D printer is better than that of the traditional PID control method. It has the advantages of small overshoot, fast response and more stable control effect. © 2020, Jilin University Press. All right reserved.
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页码：77 / 83
页数：6
相关论文
共 12 条
  • [1] Berman B., 3-D printing: the new industrial revolution, Business Horizons, 55, 2, pp. 155-162, (2012)
  • [2] Shen X.-W., Research on simulation analysis and condition recognition technique of warp deformation in FDM 3D printing, (2018)
  • [3] Xu Y., Zheng Y., Du Y., Et al., Adaptive condition predictive-fuzzy PID optimal control of start-up process for pumped storage unit at low head area, Energy Conversion and Management, 177, pp. 592-604, (2018)
  • [4] Guo X., Wang J., Liao F., Et al., Neuroadaptive quantized PID sliding-mode control for heterogeneous vehicular platoon with unknown actuator deadzone, International Journal of Robust and Nonlinear Control, 29, 1, pp. 188-208, (2019)
  • [5] Gaidhane P.J., Nigam M.J., Kumar A., Et al., Design of interval type-2 fuzzy precompensated PID controller applied to two-DOF robotic manipulator with variable payload, ISA Transactions, 89, pp. 169-185, (2019)
  • [6] Liu X.-F., Research on synchronous control technology of dual-motor in crawler crane lifting system, (2012)
  • [7] Li J., Yu C.-X., Vehicle chassis integrated control system based on fuzzy and PID, Journal of Jilin University (Engineering and Technology Edition), 43, pp. 509-513, (2013)
  • [8] Cao J.-H., Kong F.-S., Ran Y.-Z., Et al., Back pressure controller design of air compressor based on fuzzy self-adaptive PID control, Journal of Jilin University (Engineering and Technology Edition), 48, 3, pp. 781-786, (2018)
  • [9] Chehadeh M.S., Boiko I., Design of rules for in-flight non-parametric tuning of PID controllers for unmanned aerial vehicles, Journal of the Franklin Institute, 356, 1, pp. 474-491, (2019)
  • [10] Wang J., Yang G., Data-driven approach to accommodating multiple simultaneous sensor faults in variable-gain PID systems, IEEE Transactions on Industrial Electronics, 66, 4, pp. 3117-3126, (2019)
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