Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

An Adaptive Fuzzy Predictive Controller with Hysteresis Compensation for Piezoelectric Actuators

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Piezoelectric actuators (PEAs) are the pivotal components of many nanopositioning systems because of their superiorities in bandwidth, mechanical force, and precision. Unfortunately, the intrinsic nonlinear property, hysteresis, makes it difficult to achieve the precise control of PEAs. Considering this drawback, diversified feedback control approaches have been studied in the literature. Inspired by the idea that the involvement of feedforward terms can upgrade the tracking performance, our previous conference paper proposed a novel feedforward–feedback control approach (model predictive control with hysteresis compensation). Following the previous work, an adaptive fuzzy predictive controller with hysteresis compensation is further studied in this paper. The major improvement of the proposed method is the employment of adaptive fuzzy model, by which the dynamic model of PEAs is able to adjust in real time, resulting in a better control performance. To validate the effectiveness of the proposed method, extensive experiments are conducted on a Physik Instrumente P-753.1CD piezoelectric nanopositioning stage. Comparisons with several existing control approaches are carried out, and the root mean square tracking error of the proposed method is reduced to 30% of that under the previously proposed neural network model–based predictive control, when tracking 100 Hz sinusoidal reference.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Wu JW, Lin YT, Lo YT, Liu WC, Chang KY, Liu DW, Fu LC. Effective tilting angles for a dual probes AFM system to achieve high-precision scanning. IEEE/ASME Trans Mech 2016;21(5):2512–2521.

    Google Scholar 

  2. Zhang L, Yao K, Keikha E, Chen YF, Rahman MA, AI Mamun A, Bhatia CS. Dual-stage nanopositioning scheme for 10 Tbit/in 2 hard disk drives with a shear-mode piezoelectric single-crystal microactuator. IEEE Trans Magn 2015;51(4):1–9.

    Google Scholar 

  3. Qin YD, Shirinzadeh B, Tian YL, Zhang DW, Bhagat U. Design and computational optimization of a decoupled 2-DoF monolithic mechanism. IEEE/ASME Trans Mech 2014;19(3):872–881.

    Google Scholar 

  4. Cheng L, Liu WC, Yang CG, Hou ZG, Huang TW, Tan M. 2017. Neural network based modeling and control of piezoelectric-actuated stick-slip devices. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/TIE.2017.2740826.

  5. Xu QS. Robust impedance control of a compliant microgripper for high-speed position/force regulation. IEEE Trans Ind Electron 2015;62(2):1201–1209.

    Google Scholar 

  6. Ruiyue O, Jayawardhana B. Absolute stability analysis of linear systems with Duhem hysteresis operator. Automatica 2014;50(7):1860–1866.

    MathSciNet  MATH  Google Scholar 

  7. Habineza D, Rakotondrabe M, Le Gorrec Y. Bouc-Wen modeling and feedforward control of multivariable hysteresis in piezoelectric systems: application to a 3-DoF piezotube scanner. IEEE Trans Contr Syst Tech 2015;23(5): 1797–1806.

    Google Scholar 

  8. Li Z, Zhang XY, Su CY, Chai TY. Nonlinear control of systems preceded by Preisach hysteresis description: a prescribed adaptive control approach. IEEE Trans Contr Syst Tech 2016;24(2):451–460.

    Google Scholar 

  9. Gu GY, Zhu LM, Su CY. Modeling and compensation of asymmetric hysteresis nonlinearity for piezoceramic actuators with a modified Prandtl-Ishlinskii model. IEEE Trans Ind Electron 2014;61(3):1583–1595.

    Google Scholar 

  10. Liu YF, Shan JJ, Meng Y, Zhu DF. Modeling and identification of asymmetric hysteresis in smart actuators: a modified MS model approach. IEEE/ASME Trans Mech 2016;21(1):38–43.

    Google Scholar 

  11. Tian LZ, Wu JH, Xiong ZH, Ding H. Precise motion control of piezoelectric actuators using modified ZPETC-based composite controller. Proceedings of the IEEE/ASMEinternational conference on advanced intelligent mechatronics, pp. 967–972; 2014.

  12. Ahmad I, Abdurraqeeb AM. H infinity control design with feed-forward compensator for hysteresis compensation in piezoelectric actuators. Automatika 2017;57(3):691–702.

    Google Scholar 

  13. Liu W, Cheng L, Hou ZG, Tan M. An active disturbance rejection controller with hysteresis compensation for piezoelectric actuators. Proceedings of the 12th World Congress on Intelligent Control and Automation; 2016. p. 2148–2153.

  14. Ma H, Wu J, Xiong Z. Discrete-time sliding-mode control with improved quasi-sliding-mode domain. IEEE Trans Ind Electron 2016;63(10):6292–6304.

    Google Scholar 

  15. Chen X, Su CY, Li Z, Yang F. Design of implementable adaptive control for micro/nano positioning system driven by piezoelectric actuator. IEEE Trans Ind Electron 2016;63(10):6471– 6481.

    Google Scholar 

  16. Gu GY, Zhu LM, Su CY, Ding H, Fatikow S. Modeling and control of piezo-actuated nanopositioning stages: a survey. IEEE Trans Autom Sci Eng 2016;13(1):313–332.

    Google Scholar 

  17. Liu L, Tan KK, Lee TH. Multirate-based composite controller design of piezoelectric actuators for high-bandwidth and precision tracking. IEEE Trans Contr Syst Tech 2014;22(2):816–821.

    Google Scholar 

  18. Janaideh MA, Rakotondrabe M, Aljanaideh O. Further results on hysteresis compensation of smart micropositioning systems with the inverse Prandtl-Ishlinskii compensator. IEEE Trans Contr Syst Tech 2016;24(2): 428–439.

    Google Scholar 

  19. Zhang XY, Zhi L, Su CY, Lin Y, Fu YL. 2016 Implementable adaptive inverse control of hysteretic systems via output feedback with application to piezoelectric positioning stages. IEEE Trans Ind Electron 2016;63(9): 5733–5743.

    Google Scholar 

  20. Edardar M, Tan XB, Khalil HK. Designand analysis of sliding mode controller under approximate hysteresis compensation. IEEE Trans Contr Syst Tech 2014;23(2):598–608.

    Google Scholar 

  21. Fukuda T, Nakajima M, Pou L, Ahmad M. Bringing the nanolaboratory inside electron microscopes. IEEE Nanotechnology Magazine 2008;2(2):18–31.

    Google Scholar 

  22. Van Overloop PJ, Maestre JM, Sadowska AD, Camacho EF, De Schutter B. Human-in-the-loop model predictive control of an irrigation canal. IEEE Contr Syst Mag 2015;35(4):19–29.

    MathSciNet  Google Scholar 

  23. Boxhammer M, Altmannshofer S. Model predictive control in pulsed electrochemical machining. J Process Control 2014;24(1):296–303.

    Google Scholar 

  24. Chen Y, Li Z, Kong H, Ke F. Model predictive tracking control of nonholonomic mobile robots with coupled input constraints and unknown dynamics. IEEE Trans Ind Inform 2019;15(6):3196–3205.

    Google Scholar 

  25. Cheng L, Hou ZG, Tan M. Constrained multi-variable generalized predictive control using a dual neural network. Neural Comput and Appl 2007;16(6):505–512.

    Google Scholar 

  26. Cheng L, Liu W, Yang C, Hou ZG, Huang T, Tan M. A neural-network-based controller for piezoelectric-actuated stick-slip devices. IEEE Trans Ind Electron 2018;65(3):2598–2607.

    Google Scholar 

  27. Liu W, Cheng L, Hou Z G, Tan M. An inversion-free model predictive control with error compensation for piezoelectric actuators. Proceedings of the American Control Conference, pp. 5489–5494; 2015.

  28. Cheng L, Liu WC, Hou ZG, Yu JZ, Tan M. Neural-network-based nonlinear model predictive control for piezoelectric actuators. IEEE Trans Ind Electron 2015;62(12):7717–7727.

    Google Scholar 

  29. Liu WC, Cheng L, Yu JZ, Hou ZG, Tan M. An inversion-free predictive controller for piezoelectric actuators based on a dynamic linearized neural network model. IEEE/ASME Trans Mech 2016;21(1):214–226.

    Google Scholar 

  30. Liu WC, Cheng L, Wang HM, Hou ZG, Tan M. An inversion-free fuzzy predictive control for piezoelectric actuators. Proceedings of the Chinese Control and Decision Conference. CCDC, Qingdao, China. pp. 953-958; 2015.

  31. Cheng L, Liu WC, Hou ZG, Huang TW, Yu JZ, Tan M. An adaptive Takagi-Sugeno fuzzy model based predictive controller for piezoelectric actuators. IEEE Trans Ind Electron 2017;64(4):3048–3058.

    Google Scholar 

  32. Wang A, Cheng L. A composite controller for piezoelectric actuators with model predictive control and hysteresis compensation. Proceedings of the International Conference on Life System Modeling and Simulation and International Conference on Intelligent Computing for Sustainable Energy and Environment. pp. 740–750; 2017.

  33. Adriaens HJMTA, de Koning WL, Banning R. Modeling piezoelectric actuators. IEEE/ASME Trans Mech 2000;5(4):331–341.

    Google Scholar 

  34. Cao Y, Chen XB. A novel discrete ARMA-based model for piezoelectric actuator hysteresis. IEEE/ASME Trans Mech 2012;17(4):737–744.

    MathSciNet  Google Scholar 

  35. Cao Y, Cheng L, Chen XB, Peng JY. An inversion-based model predictive control with an integral-of-error state variable for piezoelectric actuators. IEEE/ASME Trans Mech. 2013;18(3):895–904.

    Google Scholar 

  36. Kaiser MS, Chowdhury ZI, Al Mamun S, Hussain A, Mahmud M. A neuro-fuzzy control system based on feature extraction of surface electromyogram signal for solar-powered wheelchair. Congn Comput 2016;8(5): 946–954.

    Google Scholar 

  37. Nian XH, Sun MP, Guo H, Wang HB, Dai LQ. Observer-based stabilization control of time-delay T-S fuzzy systems via the non-uniform delay partitioning approach. Congn Comput 2017;9(2):225–236.

    Google Scholar 

  38. Chen FY, Jiang B, Tao G. Fault self-repairing flight control of a small helicopter via fuzzy feedforward and quantum control techniques. Congn Comput 2012;4(4):543–548.

    Google Scholar 

  39. Tatjewski P. 2007. Advanced control of industrial processes: structures and algorithms Springer Science and Business Media.

  40. Lennart L. System identification: theory for the user, 2nd ed. Englewood Cliffs: Prentice-Hall; 1999.

    MATH  Google Scholar 

  41. Norgaard M, Ravn O, Poulsen NK, Hansen LK. Neural networks for modelling and control of dynamic systems. Berlin Heidelberg: Springer; 2000.

    MATH  Google Scholar 

  42. Paleologu C, Benesty J, Ciochina S. A robust variable forgetting factor recursive least-squares algorithm for system identification. IEEE Signal Processing Letters 2008;15:597–600.

    Google Scholar 

  43. Babuska R. 2000. Fuzzy modelling and identification toolbox. Control engineering laboratory, faculty of information technology and systems. Delft university of technology, Delft, The Netherlands.

  44. Li P, Li P, 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.

    Google Scholar 

  45. Fan B, Yang Q, Jagannathan S, Sun Y. Output-constrained control of nonaffine multiagent systems with partially unknown control directions. IEEE Transactions on Automatic Control 2019;64(9):3936–3942.

    MathSciNet  MATH  Google Scholar 

  46. Fan B, Yang Q, Jagannathan S, Sun Y. Asymptotic tracking controller design for nonlinear systems with guaranteed performance. IEEE Transactions on Cybernetics 2018;48(7):2001–2011.

    Google Scholar 

  47. Li Z, Huang B, Ajoudani A, Yang C, Su CY, Bicchi A. Asymmetric bimanual coordinate control of dual-arm exoskeleton robots for human cooperative manipulations. IEEE Transactions on Robotics 2018;34 (1):264–271.

    Google Scholar 

  48. Wang H, Huang T, Liao X, Abu-Rub H, Chen G. Reinforcement learning for constrained energy trading games with incomplete information. IEEE Transactions on Cybernetics 2017;47(10):3404–3416.

    Google Scholar 

  49. Wang X, Li C, Huang T, Chen L. Dual-stage impulsive control for synchronization of memristive chaotic neural networks with discrete and continuously distributed delays. Neurocomputing 2015;149:621–628.

    Google Scholar 

Download references

Funding

This study was funded by the National Natural Science Foundation of China (Grants 61873268, U1913209, 61861136009) and Beijing Natural Science Foundation (Grant JQ19020).

Author information

Authors and Affiliations

  1. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Ang Wang, Long Cheng & Zeng-Guang Hou

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China

    Ang Wang, Long Cheng & Zeng-Guang Hou

  3. School of Automation Science and Engineering, South China University of Technology, Wushan Road, Guangzhou, 510640, China

    Chenguang Yang

Authors
  1. Ang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Long Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chenguang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zeng-Guang Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Long Cheng.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, A., Cheng, L., Yang, C. et al. An Adaptive Fuzzy Predictive Controller with Hysteresis Compensation for Piezoelectric Actuators. Cogn Comput 12, 736–747 (2020). https://doi.org/10.1007/s12559-020-09722-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-020-09722-8

Keywords

Search

Navigation