Volume 42 Issue 5
Oct.  2022
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Article Navigation >  Chinese Journal of Space Science  > 2022  >  42(5): 952-960
LU Xiaoxiao, LIU Xiaoke, LI Hu, ZHENG Fu, SUN Zhibin, YU Qiang. RBF Neural Network in Electrostatic Levitation Position Control (in Chinese). Chinese Journal of Space Science, 2022, 42(5): 952-960 doi: 10.11728/cjss2022.05.210927103
Citation: LU Xiaoxiao, LIU Xiaoke, LI Hu, ZHENG Fu, SUN Zhibin, YU Qiang. RBF Neural Network in Electrostatic Levitation Position Control (in Chinese). Chinese Journal of Space Science, 2022, 42(5): 952-960 doi: 10.11728/cjss2022.05.210927103
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RBF Neural Network in Electrostatic Levitation Position Control

doi: 10.11728/cjss2022.05.210927103 cstr: 32142.14.cjss2022.05.210927103
  • 1.

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • 2.

    School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049

  • Received Date: 2021-09-27
  • Accepted Date: 2022-03-15
  • Rev Recd Date: 2022-05-11
    • Available Online: 2022-09-19
    Abstract
  • With the characteristics of nonlinearity and time variation, the position control system of electrostatic levitation facility cannot suppress the disturbance effectively by using traditional control methods. To solve this problem, a RBF-PID control strategy combining neural network and PID control method was proposed. Firstly, the mechanical analysis on the spherical sample was researched, and the mechanism model of the position control system of electrostatic levitation was deduced. Then, the position control system of electrostatic levitation was constructed, based on the RBF-PID controller. The control parameters were adjusted in real-time according to the simulation results. The simulation results show that it takes 0.12 s to make the sample stable when its surface charge suddenly changes from 10–9 C to 3×10–9 C. The experimental results show that when the sample is in the heated status, the mean absolute error of control system is 0.0416 mm by adjusting the parameters in real time, and the control effect is 70% better than that of traditional PID controller .The proposed RBF-PID control strategy has a high identification accuracy, and it has stronger robustness and stability compared with the traditional PID control strategy.

     

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    • References(24)
  • [1]
    孙一宁, 王飞龙, 于强, 等. 静电悬浮条件下的材料典型热物理性质测量[J]. 材料导报, 2016, 30(S2): 253-258

    SUN Yining, WANG Feilong, YU Qiang, et al. Thermophysical property measurements by electrostatic levitation in material science[J]. Materials Review, 2016, 30(S2): 253-258
    [2]
    WANG F L, DAI B, LIU X F, et al. Containerless heating process of a deeply undercooled metal droplet by electrostatic levitation[J]. Chinese Physics Letters, 2015, 32(11): 114101 doi: 10.1088/0256-307X/32/11/114101
    [3]
    MOHR M, WUNDERLICH R K, KOCH S, et al. Surface tension and viscosity of Cu50Zr50 measured by the oscillating drop technique on board the international space station[J]. Microgravity Science and Technology, 2019, 31(2): 177-184 doi: 10.1007/s12217-019-9678-1
    [4]
    China Manned Space. High quality domestic products, strong as the sky![EB/OL]. (2021-07-21)[2021-09-25]. http://www.cmse.gov.cn/kjkx/kjkxyjyyy/202107/t20210726_48473.html
    [5]
    RULISON A J, WATKINS J L, ZAMBRANO B. Electrostatic containerless processing system[J]. Review of Scientific Instruments, 1997, 68(7): 2856-2863 doi: 10.1063/1.1148208
    [6]
    ISHIKAWA T, KOYAMA C, PARADIS P F, et al. Densities of liquid Re, Os, and Ir, and their temperature dependence measured by an electrostatic levitator[J]. International Journal of Refractory Metals and Hard Materials, 2020, 92: 105305 doi: 10.1016/j.ijrmhm.2020.105305
    [7]
    HERLACH D. Crystal nucleation and dendrite growth of metastable phases in undercooled melts[J]. Journal of Alloys and Compounds, 2011, 509(S1): S13-S17
    [8]
    LEE G W, JEON S, PARK C, et al. Crystal-liquid interfacial free energy and thermophysical properties of pure liquid Ti using electrostatic levitation: Hypercooling limit, specific heat, total hemispherical emissivity, density, and interfacial free energy[J]. The Journal of Chemical Thermodynamics, 2013, 63: 1-6 doi: 10.1016/j.jct.2013.03.012
    [9]
    HU L, WANG H P, XIE W J, et al. Electrostatic levitation under the single-axis feedback control condition[J]. Science China Physics, Mechanics and Astronomy, 2010, 53(8): 1438-1444 doi: 10.1007/s11433-010-4068-0
    [10]
    ZOU Z Z, LUO X H, YU Q. Droplet image super resolution based on sparse representation and kernel regression[J]. Microgravity Science and Technology, 2018, 30(3): 321-329 doi: 10.1007/s12217-018-9597-6
    [11]
    陈东阳, 郭清远, 董文博, 等. 基于高速视觉的静电悬浮控制系统[J]. 光学 精密工程, 2019, 27(11): 2343-2353 doi: 10.3788/OPE.20192711.2343

    CHEN Dongyang, GUO Qingyuan, DONG Wenbo, et al. Control system of electrostatic levitation based on high-speed vision[J]. Optics and Precision Engineering, 2019, 27(11): 2343-2353 doi: 10.3788/OPE.20192711.2343
    [12]
    NAKAMURA T, AWA Y, SHIMOJI H, et al. Control system of electrostatic levitation furnace[J]. Acta Astronautica, 2002, 50(10): 609-614 doi: 10.1016/S0094-5765(01)00219-3
    [13]
    MEISTER T, WERNER H, LOHOEFER G, et al. Gain-scheduled control of an electrostatic levitator[J]. Control Engineering Practice, 2003, 11(2): 117-128 doi: 10.1016/S0967-0661(02)00102-8
    [14]
    NUELLA I, CHENG C, CHIU M S. Adaptive PID controller design for nonlinear systems[J]. Industrial & Engineering Chemistry Research, 2009, 48(10): 4877-4883
    [15]
    QIAO J F, LI F, YANG C L, et al. A self-organizing RBF neural network based on distance concentration immune algorithm[J]. IEEE/CAA Journal of Automatica Sinica, 2020, 7(1): 276-291 doi: 10.1109/JAS.2019.1911852
    [16]
    郭益深, 陈力. 漂浮基姿态受控空间机械臂关节运动的自适应神经网络控制[J]. 空间科学学报, 2008, 28(2): 173-179 doi: 10.11728/cjss2008.02.173

    GUO Yishen, CHEN Li. Adaptive neural network control of free-floating space manipulator with an attitude controlled base[J]. Chinese Journal of Space Science, 2008, 28(2): 173-179 doi: 10.11728/cjss2008.02.173
    [17]
    RAHIMZADEH H, SADEGHI M, GHASEMI-VARNAMKHASTI M, et al. On the feasibility of metal oxide gas sensor based electronic nose software modification to characterize rice ageing during storage[J]. Journal of Food Engineering, 2019, 245: 1-10 doi: 10.1016/j.jfoodeng.2018.10.001
    [18]
    LI Y, LIU M Y, ZHANG X J, et al. Global approximation based adaptive RBF neural network control for supercavitating vehicles[J]. Journal of Systems Engineering and Electronics, 2018, 29(4): 797-804 doi: 10.21629/JSEE.2018.04.14
    [19]
    MUNRO K, MILLER T H, MARTINS C P B, et al. Artificial neural network modelling of pharmaceutical residue retention times in wastewater extracts using gradient liquid chromatography-high resolution mass spectrometry data[J]. Journal of Chromatography A, 2015, 1396: 34-44 doi: 10.1016/j.chroma.2015.03.063
    [20]
    周扬扬, 韦韧, 王超, 等. 静电悬浮位置控制系统设计与实现[J]. 空间科学学报, 2011, 31(5): 675-681 doi: 10.11728/cjss2011.05.675

    ZHOU Yangyang, WEI Ren, WANG Chao, et al. Design and implementation of electrostatic levitation position control system[J]. Chinese Journal of Space Science, 2011, 31(5): 675-681 doi: 10.11728/cjss2011.05.675
    [21]
    LIU N J, CAI Z H, ZHAO J, et al. Predictor-based model reference adaptive roll and yaw control of a quad-tiltrotor UAV[J]. Chinese Journal of Aeronautics, 2020, 33(1): 282-295 doi: 10.1016/j.cja.2019.08.001
    [22]
    李明, 封航, 张延顺. 基于UMAC的RBF神经网络PID控制[J]. 北京航空航天大学学报, 2018, 44(10): 2063-2070 doi: 10.13700/j.bh.1001-5965.2017.0777

    LI Ming, FENG Hang, ZHANG Yanshun. RBF neural network tuning PID control based on UMAC[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(10): 2063-2070 doi: 10.13700/j.bh.1001-5965.2017.0777
    [23]
    ATTARAN S M, YUSOF R, SELAMAT H. A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system[J]. Applied Thermal Engineering, 2016, 99: 613-624 doi: 10.1016/j.applthermaleng.2016.01.025
    [24]
    SHI K J, LI B, WANG F M, et al. Research on the RBF-PID control method for the motor actuator used in a UHV GIS disconnector[J]. The Journal of Engineering, 2019, 2019(16): 2013-2017 doi: 10.1049/joe.2018.8728
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