Adaptive Control Algorithm Improving the Stability of Micro Force Measurement System

  • Yelong Zheng
  • Xiaoli Yang
  • Meirong Zhao
  • Tao Guan
  • Meihua Jiang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)


Based on the electrostatics principle, the measurement and realization of micro forces in the order of 10-6 N to 10-4 N (the resolution is 10-8 N) can be realized and it can be traced back to the SI such as length, voltage, capacitance, etc. Because the stiffness and damping ratio of the system are very small, both the external interference and the parameter variation would make the system unstable so that it is hardly to make the system get optimal by using the traditional PID control algorithm. This dissertation introduces an adaptive control algorithm based on minimum variance. Adjust the parameter values of the controller continuously to make the transient response and the output of the control object get optimal when the measurement system working. The system performance is tested by measuring standard weights of 5, 10 and 20 mg. The result shows that it can not only speed up the convergence but also reduce residual vibration, decreasing variance to 0.01 μm.


Electrostatic force Traceability Adaptive Control Measurement Minimum variance 



This work was supported by National Natural Science Foundation (No. 51175377) and Tianjin Natural Science Foundation (No. 12JCQNJC02700).


  1. 1.
    Newell DB, Kramar JA, Pratt JR, Smith DT, Williams ER (2003) The NIST microforce realization and measurement project. IEEE Trans Instrum Meas 52(2):508–511. doi: 10.1109/tim.2003.810032 CrossRefGoogle Scholar
  2. 2.
    Fujii Y (2007) Method of generating and measuring static small force using down-slope component of gravity. Rev Sci Instrum 78(6):066104. doi: 10.1063/1.2746823 CrossRefGoogle Scholar
  3. 3.
    Pratt JR, Newell DB, Kramar JA, Mulholland J, Whitenton E (2003) Probe-force calibration experiments using the NIST electrostatic force balance. Proceedings of American Society for Precision Engineering, Winter Topical Meeting, University of Florida, vol 9(61), pp 161–5Google Scholar
  4. 4.
    Sendjaja AY, Kariwala V (2009) Achievable PID performance using sums of squares programming. J Process Control 19(6):1061–1065. doi: 10.1016/j.jprocont.2008.12.005 CrossRefGoogle Scholar
  5. 5.
    Harris TJ (1989) Assessment of control loop performance. Can J Chem Eng 67(5):856–861. doi: 10.1002/acs.767 CrossRefGoogle Scholar
  6. 6.
    Boyd S, Barrat C (1991) Linear control design. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  7. 7.
    Huang B (2003) A pragmatic approach towards assessment of control loop performance. Int J Adapt Control Signal Process 17:589–608. doi: 10.1002/acs.767 CrossRefMATHGoogle Scholar
  8. 8.
    Lofberg J (2004) YALMIP: a toolbox for modeling and optimization in MATLAB. Proceedings of IEEE international symposium on computer-aided control system design, Taipei, Taiwan, No. 04TH8770, pp 284–89Google Scholar
  9. 9.
    Kariwala V (2007) Fundamental limitation on achievable decentralized performance. Automatica 43(10):1849–1854. doi: 10.1016/j.automatica.2007.03.004 CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yelong Zheng
    • 1
  • Xiaoli Yang
    • 2
  • Meirong Zhao
    • 1
  • Tao Guan
    • 1
  • Meihua Jiang
    • 1
  1. 1.State Key Laboratory of Precision Measuring Technology and InstrumentsTianjin UniversityTianjinChina
  2. 2.China Petroleum Technology & Development CorporationBeijingChina

Personalised recommendations