Journal of Systems Science and Complexity

, Volume 32, Issue 5, pp 1340–1357 | Cite as

Simulation and Experiment Based on FSMLC Method with EUPI Hysteresis Compensation for a Piezo-Driven Micro Position Stage

  • Jinhai Gao
  • Lina HaoEmail author
  • Hongtai Cheng
  • Ruimin Cao
  • Zhiyong Sun


Micro/nano positioning technologies have been attractive for decades in industrial and scientific applications fields. The actuators have inherent hysteresis that can cause system unexpected behave in some extend. In this research, the authors used extented unparallel Prandtl-Ishlinshii (EUPI) models to represent the input-output relationship of a piezo-driven micro position stage. Integral inverse (I-I) compensator is used for compensating the hysteresis characteristics of the micro positioning stage and compared with direct inverse (D-I) compensator and inverse model (I-M) compensator. However, the accuracy and the robustness of the I-I compensator are worse when there is noisy in the system, a novel sliding-mode-like-control with EUPI (SMLC-EUPI) method was proposed and analyzed by different trajectory tracking experiments in Matlab environment. Though the above strategies can alleviate most deviation, the adjustment of the SMLC’s parameters is very complex. So the fuzzy method is used to adjust these parameters and be verified by trajectory tracking experiments. Finally, for validating the proposed control method, the paper did the corresponding experiment in microscope with CMOS and obtained convincing results.


EUPI fuzzy hysteresis compensation micro position stage sliding-mode-like-control 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Yang R, Song B, Sun Z, et al., Cellular level robotic surgery: Nanodissection of intermediate filaments in live keratinocytes, Nanomedicine: Nanotechnology, Biology and Medicine, 2015, 11(1): 137–145.CrossRefGoogle Scholar
  2. [2]
    Nelson B J, Dong L, and Arai F, Micro-/Nanorobots, Springer Handbook of Robotics, Springer, Cham, 2016, 671–716.Google Scholar
  3. [3]
    Liu W, Cheng L, Zhou C, et al., Neural-network based model predictive control for piezoelectric-actuated stick-slip micro-positioning devices, IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2016, 1312–1317.Google Scholar
  4. [4]
    Choi K B, Lee, and J J, Analysis and design of linear parallel compliant stage for ultra-precision motion based on 4-PP flexural joint mechanism, International Conference on Smart Manufacturing Application, 2008, 35–38.Google Scholar
  5. [5]
    Lin S, Jia Y, Lei I P, et al., Design and optimization of a long-stroke compliant micropositioning stage driven by voice coil motor, 12th International Conference on Control Automation Robotics & Vision (ICARCV), 2012, 1716–1721.Google Scholar
  6. [6]
    Qi Q and Du R, A vision based micro-assembly system for assembling components in mechanical watch movements, International Symposium on Optomechatronic Technologies (ISOT), 2010, 1–5.Google Scholar
  7. [7]
    Kunt E D, Naskali A T, Khalil I S M, et al., Design and development of workstation for microparts manipulation and assembly, Turkish Journal of Electrical Engineering & Computer Sciences, 2011, 19(6): 973–992.Google Scholar
  8. [8]
    Wu Z and Xu Q, Design and optimization of a compact XY parallel micro/nano-positioning stage with stacked structure, 2nd International Conference on Advanced Robotics and Mechatronics (ICARM), 2017, 558–563.Google Scholar
  9. [9]
    Hao L N and Cao R M, A Large-Workspace Fast Response X-Y Micro-Plantform with Two Parallel Displacement Amplification Mechanism, CN105006254A.Google Scholar
  10. [10]
    Cao R M, Mechanism and Measurement & Control System Design of Micro-Positioning Piezo-Stage Actuated by PZT, Northeastern University of China, Shenyang, 2016.Google Scholar
  11. [11]
    Jaffe B, Piezoelectric Ceramics, Elsevier, 2012.Google Scholar
  12. [12]
    Damjanovic D, Hysteresis in piezoelectric and ferroelectric materials, The Science of Hysteresis, 2006, 3: 337–465.CrossRefGoogle Scholar
  13. [13]
    Esbrook A, Tan X, and Khalil H K, Control of systems with hysteresis via servocompensation and its application to nanopositioning, IEEE Transactions on Control Systems Technology, 2013, 21(3): 725–738.CrossRefGoogle Scholar
  14. [14]
    Kenton B J and Leang K K, Design and control of a three-axis serial-kinematic high-bandwidth nanopositioner, IEEE/ASME Transactions on Mechatronics, 2012, 17(2): 356–369.CrossRefGoogle Scholar
  15. [15]
    Li Y and Xu Q, Design and robust repetitive control of a new parallel-kinematic XY piezostage for micro/nanomanipulation, IEEE/ASME Transactions on Mechatronics, 2012, 17(6): 1120–1132.CrossRefGoogle Scholar
  16. [16]
    Janaideh M A, Rakheja S, and Su C Y, An analytical generalized Prandtl-Ishlinskii model inversion for hysteresis compensation in micropositioning control, IEEE/ASME Transactions on Mechatronics, 2011, 16(4): 734–744.CrossRefGoogle Scholar
  17. [17]
    Li Z, Su C Y, and Chai T, Compensation of hysteresis nonlinearity in magnetostrictive actuators with inverse multiplicative structure for Preisach model, IEEE Transactions on Automation Science and Engineering, 2014, 11(2): 613–619.CrossRefGoogle Scholar
  18. [18]
    Sun Z, Song B, Xi N, et al., Compensating asymmetric hysteresis for nanorobot motion control, IEEE International Conference on Robotics and Automation (ICRA), 2015, 3501–3506.Google Scholar
  19. [19]
    Sun Z Y, Chen L L, Song B, et al., Asymmetric hysteresis modeling and compensation approach for nanomanipulation system motion control considering working-range effect, IEEE Transactions on Industrial Electronics, 2017, 64(7): 5513–5523.CrossRefGoogle Scholar
  20. [20]
    Sun Z, Xi N, Cheng Y, et al., Exact inversion of discrete Preisach model for compensating complex hysteresis in AFM based nanomanipulator, IEEE 17th International Conference on Nanotechnology (IEEE-NANO), 2017.Google Scholar
  21. [21]
    Sun Z, Cheng Y, Xi N, et al., Frequency domain approach for dynamics identification of the actuator with asymmetric hysteresis, IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2017, 364–369.Google Scholar
  22. [22]
    Yang Y, Xi N, Sun Z, et al., Nanorobot enabled in situ sensing molecular interactions for drug discovery, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, 5285–5290.Google Scholar
  23. [23]
    Sun Z, Song B, Xi N, et al., Systematic hysteresis compensator design based on extended unparallel Prandtl-Ishlinskii model for SPM imaging rectification, IFAC-PapersOnLine, 2017, 50(1): 10901–10906.CrossRefGoogle Scholar
  24. [24]
    Sun Z, Hao L, Song B, et al., Periodic reference tracking control approach for smart material actuators with complex hysteretic characteristics, Smart Materials and Structures, 2016, 25(10): 105029.CrossRefGoogle Scholar
  25. [25]
    Xiang C, Yang H, Sun Z, et al., The design, hysteresis modeling and control of a novel SMA-fishing-line actuator, Smart Materials and Structures, 2017, 26(3): 037004.CrossRefGoogle Scholar
  26. [26]
    Hao L, Yang H, Sun Z, et al., Modeling and compensation control of asymmetric hysteresis in a pneumatic artificial muscle, Journal of Intelligent Material Systems and Structures, 2017, 28(19): 2769–2780.CrossRefGoogle Scholar
  27. [27]
    Sun Z, Hao L, Chen W, et al., A novel discrete adaptive sliding-mode-like control method for ionic polymer-metal composite manipulators, Smart Materials and Structures, 2013, 22(9): 095027.CrossRefGoogle Scholar
  28. [28]
    Song G, Zhao J, Zhou X, et al., Tracking control of a piezoceramic actuator with hysteresis compensation using inverse Preisach model, IEEE/ASME Transactions on Mechatronics, 2005, 10(2): 198–209.CrossRefGoogle Scholar
  29. [29]
    Xia S and Li Y, Modeling and high dynamic compensating the rate-dependent hysteresis of piezoelectric actuators via a novel modified inverse Preisach model, IEEE Transactions on Control Systems Technology, 2013, 21(5): 1549–1557.CrossRefGoogle Scholar
  30. [30]
    Gan M G, Qiao Z, and Li Y L, Sliding mode control with perturbation estimation and hysteresis compensator based on bouc-wen model in tackling fast-varying sinusoidal position control of a piezoelectric actuator, Journal of Systems Science & Complexity, 2016, 29(2): 367–381.MathSciNetCrossRefGoogle Scholar
  31. [31]
    Zhang Y and Xu Q, Adaptive sliding mode control with parameter estimation and kalman filter for precision motion control of a piezo-driven microgripper, IEEE Transactions on Control Systems Technology, 2017, 25(2): 728–735.CrossRefGoogle Scholar
  32. [32]
    Xu Q, Precision motion control of piezoelectric nanopositioning stage with chattering-free adaptive sliding mode control, IEEE Transactions on Automation Science and Engineering, 2017, 14(1): 238–248.CrossRefGoogle Scholar
  33. [33]
    Xu Q, Continuous integral terminal third-order sliding mode motion control for piezoelectric nanopositioning system, IEEE/ASME Transactions on Mechatronics, 2017, 22(4): 1828–1838.CrossRefGoogle Scholar
  34. [34]
    Janaideh M A, Rakotondrabe M, and Aljanaideh O, Further results on hysteresis compensation of smart micropositioning systems with the inverse prandtl-ishlinskii compensator, IEEE Transactions on Control Systems Technology, 2016, 24(2): 428–439.CrossRefGoogle Scholar
  35. [35]
    Tang H and Li Y, Feedforward nonlinear PID control of a novel micromanipulator using Preisach hysteresis compensator, Robotics and Computer-Integrated Manufacturing, 2015, 34: 124–132.CrossRefGoogle Scholar
  36. [36]
    Rana M S, Pota H R, and Petersen I R, Approach for improved positioning of an atomic force microscope piezoelectric tube scanner, Micro & Nano Letters, 2014, 9(6): 407–411.CrossRefGoogle Scholar
  37. [37]
    Zhou M, He S, Hu B, et al., Modified KP model for hysteresis of magnetic shape memory alloy actuator, IETE Technical Review, 2015, 32(1): 29–36.CrossRefGoogle Scholar
  38. [38]
    Zhang Y, Yan P, and Zhang Z, Robust adaptive backstepping control for piezoelectric nano-manipulating systems, Mechanical Systems and Signal Processing, 2017, 83: 130–148.CrossRefGoogle Scholar
  39. [39]
    Li L, Song G, and Ou J, Adaptive fuzzy sliding mode based active vibration control of a smart beam with mass uncertainty, Structural Control and Health Monitoring, 2011, 18(1): 40–52.Google Scholar
  40. [40]
    Li P, Li P, and Sui Y, Adaptive fuzzy hysteresis internal model tracking control of piezoelectric actuators with nanoscale application, IEEE Transactions on Fuzzy Systems, 2016, 24(5): 1246–1254.CrossRefGoogle Scholar

Copyright information

© The Editorial Office of JSSC & Springer-Verlag GmbH Germany 2019

Authors and Affiliations

  • Jinhai Gao
    • 1
  • Lina Hao
    • 1
    Email author
  • Hongtai Cheng
    • 1
  • Ruimin Cao
    • 1
  • Zhiyong Sun
    • 2
  1. 1.School of Mechanical Engineering & AutomationNortheastern UniversityShenyangChina
  2. 2.Department of Industrial and Manufacturing Systems EngineeringThe University of Hong KongHong KongChina

Personalised recommendations