• Qingsong XuEmail author
  • Kok Kiong Tan
Part of the Advances in Industrial Control book series (AIC)


This chapter provides an introduction to the piezoelectric micro-/nano-positioning system and the concerned control problems.


Support Vector Machine Force Control Model Predictive Control Piezoelectric Actuator Little Square Support Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Abidi, K., Xu, J.X., Yu, X.: On the discrete-time integral sliding mode control. IEEE Trans. Automat. Control 52(4), 709–715 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Alkkiomaki, O., Kyrki, V., Kalviainen, H., Liu, Y., Handroos, H.: Smooth transition from motion to force control in robotic manipulation using vision. In: Proceeding of the 9th International Conference on Control, Automation, Robotics and Vision, pp. 1–6 (2006)Google Scholar
  3. 3.
    Almeida, F., Lopes, A., Abreu, P.: Force-impedance control: A new control strategy of robotic manipulators. In: Kaynak, O., Tosunoglu, S., Ang, M. (eds.) Recent Advances in Mechatronics, pp. 126–137 (1999)Google Scholar
  4. 4.
    Ang, W.T., Khosla, P.K., Riviere, C.N.: Feedforward controller with inverse rate-dependent model for piezoelectric actuators in trajectory-tracking applications. IEEE/ASME Trans. Mechatron. 12(2), 134–142 (2007)CrossRefGoogle Scholar
  5. 5.
    Bandyopadhyay, B., Fulwani, D.: High-performance tracking controller for discrete plant using nonlinear sliding surface. IEEE Trans. Ind. Electron. 56(9), 3628–3637 (2009)CrossRefGoogle Scholar
  6. 6.
    Bargiel, S., Rabenorosoa, K., Clévy, C., Gorecki, C., Lutz, P.: Towards micro-assembly of hybrid moems components on a reconfigurable silicon free-space micro-optical bench. J. Micromech. Microeng. 20(4), 045012 (2010)Google Scholar
  7. 7.
    Bartoszewicz, A.: Discrete-time quasi-sliding-mode control strategies. IEEE Trans. Ind. Electron. 45(5), 633–637 (1998)CrossRefGoogle Scholar
  8. 8.
    Bonitz, R.G., Hsia, T.C.: Internal force-based impedance control for cooperating manipulators. IEEE Trans. Robot. Automat. 12(1), 78–89 (1996)CrossRefGoogle Scholar
  9. 9.
    Boukari, A.F., Carmona, J.C., Moraru, G., Malburet, F., Chaaba, A., Douimi, M.: Piezo-actuators modeling for smart applications. Mechatronics 21(1), 339–349 (2011)CrossRefGoogle Scholar
  10. 10.
    Chen, S.P., Liaw, H.C.: Generalized impedance control of robot for assembly tasks requiring compliant manipulaiton. IEEE Trans. Ind. Electron. 43(4), 453–461 (1996)CrossRefGoogle Scholar
  11. 11.
    Chen, X., Hisayama, T.: Adaptive sliding-mode position control for piezo-actuated stage. IEEE Trans. Ind. Electron. 55(11), 3927–3934 (2008)CrossRefGoogle Scholar
  12. 12.
    Cheng, C.C., Chang, C.C., Su, T.M.: Design of model reference adaptive tracking controllers for mismatch perturbed nonlinear systems with input nonlinearity. In: Proceeding of 17th IFAC World Congress, pp. 5974–5979. Seoul, Korea (2008)Google Scholar
  13. 13.
    Clayton, G.M., Tien, S., Fleming, A.J., Moheimani, S.O.R., Devasia, S.: Inverse-feedforward of charge-controlled piezopositioners. Mechatronics 18(5–6), 273–281 (2008)CrossRefGoogle Scholar
  14. 14.
    Demetriou, M.A., Fahroo, F.: Model reference adaptive control of structurally perturbed second-order distributed parameter systems. Int. J. Robust Nonlinear Control 16(16), 773–799 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Dong, R., Tan, Y., Chen, H., Xie, Y.: A neural networks based model for rate-dependent hysteresis for piezoelectric actuators. Sens. Actuator A-Phys. 143(2), 370–376 (2008)CrossRefGoogle Scholar
  16. 16.
    Esbrook, A., Tan, X., Khalil, H.K.: Control of systems with hysteresis via servocompensation and its application to nanopositioning. IEEE Trans. Control Syst. Technol. 21(3), 725–738 (2013)CrossRefGoogle Scholar
  17. 17.
    Fleming, A.J., Leang, K.K.: Design. Springer, Modeling and Control of Nanopositioning Systems (2014)Google Scholar
  18. 18.
    Gao, B., Shao, J., Han, G., Sun, G., Yang, X., Wu, D.: Using fuzzy switching to achieve the smooth switching of force and position. Appl. Mech. Mater. 274, 638–641 (2013)CrossRefGoogle Scholar
  19. 19.
    Gao, W., Wang, Y., Homaifa, A.: Discrete-time variable structure control systems. IEEE Trans. Ind. Electron. 42(2), 117–122 (1995)CrossRefGoogle Scholar
  20. 20.
    Garcia-Gabin, W., Zambrano, D., Camacho, E.F.: Sliding mode predictive control of a solar air conditioning plant. Control Eng. Pract. 17(6), 652–663 (2009)CrossRefGoogle Scholar
  21. 21.
    Ge, P., Jouaneh, M.: Tracking control of a piezoceramic actuator. IEEE Trans. Control Syst. Technol. 4(3), 209–216 (1996)CrossRefGoogle Scholar
  22. 22.
    Hara, S., Yamada, Y.: A control method switching from servo automatic transfer to force sensorless impedance control manual positioning. In: Proceeding IECON 2007—33rd Annual Conference of the IEEE Industrial Electronics Society, pp. 292–298 (2007)Google Scholar
  23. 23.
    Huang, H.B., Sun, D., Mills, J.K., Cheng, S.H.: Robotic cell injection system with position and force control: Toward automatic batch biomanipulation. IEEE Trans. Robot. 25(3), 727–737 (2009)CrossRefGoogle Scholar
  24. 24.
    Huang, H.P., Yan, J.L., Cheng, T.H.: Development and fuzzy control of a pipe inspection robot. IEEE Trans. Ind. Electron. 57(3), 1088–1095 (2010)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Hwang, C.L.: Microprocessor-based fuzzy decentralized control of 2-D piezo-driven systems. IEEE Trans. Ind. Electron. 55(3), 1411–1420 (2008)CrossRefGoogle Scholar
  26. 26.
    Janaideh, M.A., Rakheja, S., Su, C.Y.: Experimental characterization and modeling of rate-dependent hysteresis of a piezoceramic actuator. Mechatronics 19(5), 656–670 (2009)CrossRefGoogle Scholar
  27. 27.
    Jung, S., Hsia, T.C., Bonitz, R.G.: Force tracking impedance control for robot manipulators with an unknown environment: Theory, simulation, and experiment. Int. J. Robot. Res. 20(9), 765–774 (2001)CrossRefGoogle Scholar
  28. 28.
    Jung, S., Hsia, T.C., Bonitz, R.G.: Force tracking impedance control of robot manipulators under unknown environment. IEEE Trans. Control Syst. Technol. 12(3), 474–483 (2004)CrossRefGoogle Scholar
  29. 29.
    Kim, B., Washington, G.N.: Nonlinear position control of smart actuators using model predictive sliding mode control. In: Proceeding of ASME Conference on Smart Materials, Adaptive Structures and Intelligent Systems, pp. 511–522 (2008)Google Scholar
  30. 30.
    Kim, J., Kang, B.: Micro-macro linear piezoelectric motor based on self-moving cell. Mechatronics 19(7), 1134–1142 (2009)CrossRefGoogle Scholar
  31. 31.
    Kim, J.Y., Bentsman, J.: Disturbance rejection in robust model reference adaptive control of parabolic and hyperbolic systems. In: Proceeding of 45th IEEE Conference on Decision and Control, pp. 3083–3088. San Diego, CA, USA (2006)Google Scholar
  32. 32.
    Leang, K.K., Zou, Q., Devasia, S.: Feedforward control of piezoactuators in atomic force microscope systems: Inversion-based compensation for dynamics and hysteresis. IEEE Control Syst. Mag. 19(1), 70–82 (2009)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Li, G., Wen, C., Zheng, W.X., Chen, Y.: Identification of a class of nonlinear autoregressive models with exogenous inputs based on kernel machines. IEEE Trans. Signal Process. 59(5), 2146–2159 (2012)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Li, Y., Xu, Q.: Hysteresis modeling and compensation for an XY micropositioning stage with model reference adaptive control. In: Proceeding of 48th IEEE Conference on Decision and Control, pp. 5580–5585. Shanghai, China (2009)Google Scholar
  35. 35.
    Li, Y., Xu, Q.: Adaptive sliding mode control with perturbation estimation and PID sliding surface for motion tracking of a piezo-driven micromanipulator. IEEE Trans. Control Syst. Technol. 18(4), 798–810 (2010)CrossRefGoogle Scholar
  36. 36.
    Liaw, H.C., Shirinzadeh, B.: Enhanced adaptive motion tracking control of piezo-actuated flexure-based four-bar mechanisms for micro/nano manipulation. Sens. Actuator A-Phys. 147, 254–262 (2008)CrossRefGoogle Scholar
  37. 37.
    Liaw, H.C., Shirinzadeh, B.: Robust adaptive constrained motion tracking control of piezo-actuated flexure-based mechanisms for micro/nano manipulation. IEEE Trans. Ind. Electron. 58(4), 1406–1415 (2011)CrossRefGoogle Scholar
  38. 38.
    Lin, C.J., Chen, S.Y.: Evolutionary algorithm based feedforward control for contouring of a biaxial piezo-actuated stage. Mechatronics 19(6), 829–839 (2009)CrossRefGoogle Scholar
  39. 39.
    Liu, Y.T., Chang, K.M., Li, W.Z.: Model reference adaptive control for a piezo-positioning system. Precis. Eng. 34(1), 62–69 (2010)CrossRefGoogle Scholar
  40. 40.
    Lopez-Walle, B., Gauthier, M., Chaillet, N.: Principle of a submerged freeze gripper for microassembly. IEEE Trans. Robot. 24(4), 897–902 (2008)CrossRefGoogle Scholar
  41. 41.
    Lu, W.S., Meng, Q.H.: Impedance control with adaptation for robotic manipulations. IEEE Trans. Robot. Automat. 7(3), 408–415 (1991)CrossRefGoogle Scholar
  42. 42.
    Lu, Z., Chen, P.C.Y., Lin, W.: Force sensing and control in micromanipulation. IEEE Trans. Syst. Man Cybern. Part C, Appl. Rev. 36(6), 713–724 (2006)Google Scholar
  43. 43.
    Lu, Z., Kawamura, S., Goldenberg, A.A.: An approach to sliding-mode based control. IEEE Trans. Robot. Automat. 11(5), 754–759 (1995)CrossRefGoogle Scholar
  44. 44.
    Mitic, D., Milojkovic, M., Antic, D.: Tracking system design based on digital minimum variance control with fuzzy sliding mode. In: Proceeding of 8th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services (TELSIKS 2007), pp. 494–497. Nis, Serbia (2007)Google Scholar
  45. 45.
    Mitic, D., Milosavljevic, C.: Sliding mode-based minimum variance and generalized minimum variance controls with \({O(T^2)}\) and \({O(T^3)}\) accuracy. Electr. Eng. 86(4), 229–237 (2004)CrossRefGoogle Scholar
  46. 46.
    Navarro, C., Treesatayapun, C., Baltazar, A.: Determination of the instantaneous initial contact point on a parallel gripper using a multi input fuzzy rules emulated network controller with feedback from ultrasonic and force sensors. In: Batyrshin, I., Gonzalez Mendoza, M. (eds.) Advances in Artificial Intelligence, Lecture Notes in Computer Science, vol. 7629, pp. 261–272. Springer, Berlin (2013)Google Scholar
  47. 47.
    Neelakantan, V.A., Washington, G.N., Bucknor, N.K.: Model predictive control of a two stage actuation system using piezoelectric actuators for controllable industrial and automotive brakes and clutches. J. Intell. Mater. Syst. Struct. 19(7), 845–857 (2008)CrossRefGoogle Scholar
  48. 48.
    Niksefat, N., Wu, Q., Sepehri, N.: Stable control of an electro-hydraulic actuator during contact tasks: Theory and experiments. In: Proceeding of the American Control Conference, pp. 4114–4118 (2000)Google Scholar
  49. 49.
    Orlowska-Kowalska, T., Dybkowski, M., Szabat, K.: Adaptive sliding-mode neuro-fuzzy control of the two-mass induction motor drive without mechanical sensors. IEEE Trans. Ind. Electron. 57(2), 553–564 (2010)CrossRefGoogle Scholar
  50. 50.
    Reddy, A.N., Maheshwari, N., Sahu, D.K., Ananthasuresh, G.K.: Miniature compliant grippers with vision-based force sensing. IEEE Trans. Robot. 26(5), 867–877 (2010)CrossRefGoogle Scholar
  51. 51.
    Sariola, V., Jaaskelainen, M., Zhou, Q.: Hybrid microassembly combining robotics and water droplet self-alignment. IEEE Trans. Robot. 26(6), 965–977 (2010)CrossRefGoogle Scholar
  52. 52.
    Schutter, J.D., Bruyninckx, H., Zhu, W.H., Spong, M.W.: Force control: A bird’s eye view. In: Siciliano, B. (ed.) Control Problems in Robotics and Automation: Future Directions, pp. 1–17 (1998)Google Scholar
  53. 53.
    Seki, H.: Modeling and impedance control of a piezoelectric bimorph microgripper. In: Proceeding of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, pp. 958–965. Raleigh, USA (1992)Google Scholar
  54. 54.
    Seraji, H., Colbaugh, R.: Force tracking in impedance control. Int. J. Robot. Res. 16(1), 97–117 (1997)CrossRefGoogle Scholar
  55. 55.
    Sha, D., Bajic, V.B.: Robust discrete adaptive input-output-based sliding mode controller. Int. J. Syst. Sci. 31(12), 1601–1614 (2000)CrossRefzbMATHGoogle Scholar
  56. 56.
    Sha, D., Bajic, V.B., Yang, H.: New model and sliding mode control of hydraulic elevator velocity tracking system. Simul. Pract. Theory 9(6), 365–385 (2002)CrossRefzbMATHGoogle Scholar
  57. 57.
    Shimada, N., Yoshioka, T., Ohishi, K., Miyazaki, T.: Novel force-sensor-less contact motion control for quick and smooth industrial robot motion. In: Proceeding IECON 2011—37th Annual Conference on IEEE Industrial Electronics Society, pp. 4238–4243 (2011)Google Scholar
  58. 58.
    Song, G., Zhao, J., Zhou, X., De Abreu-Garcia, J.: Tracking control of a piezoceramic actuator with hysteresis compensation using inverse preisach model. IEEE/ASME Trans. Mechatron. 10(2), 198–209 (2005)CrossRefGoogle Scholar
  59. 59.
    Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific Publishing Co., Singapore (2002)CrossRefzbMATHGoogle Scholar
  60. 60.
    Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  61. 61.
    Takahashi, T., Tsuboi, T., Kishida, T., Kawanami, Y., Shimizu, S., Iribe, M., Fukushima, T., Fujita, M.: Adaptive grasping by multi fingered hand with tactile sensor based on robust force and position control. In: Proceeding of the International Conference on Robotics and Automation, pp. 264–271 (2008)Google Scholar
  62. 62.
    Wang, C.H., Huang, D.Y.: A new intelligent fuzzy controller for nonlinear hysteretic electronic throttle in modern intelligent automobiles. IEEE Trans. Ind. Electron. 60(6), 2332–2345 (2013)CrossRefGoogle Scholar
  63. 63.
    Wang, R., Liu, G.P., Wang, W., Rees, D., Zhao, Y.B.: \({H}_{\infty }\) control for networked predictive control systems based on the switched Lyapunov function method. IEEE Trans. Ind. Electron. 57(10), 3565–3571 (2010)CrossRefGoogle Scholar
  64. 64.
    Wills, A.G., Bates, D., Fleming, A.J., Ninness, B., Moheimani, S.O.R.: Model predictive control applied to constraint handling in active noise and vibration control. IEEE Trans. Control Syst. Technol. 16(1), 3–12 (2008)CrossRefGoogle Scholar
  65. 65.
    Wu, Y., Zou, Q.: Robust-inversion-based 2-DOF control design for output tracking: Piezoelectric actuator example. IEEE Trans. Control Syst. Technol. 17(5), 1069–1082 (2009)CrossRefGoogle Scholar
  66. 66.
    Xi, Z., Hesketh, T.: Discrete time integral sliding mode control for overhead crane with uncertainties. IET Control Theory Appl. 4(10), 2071–2081 (2010)MathSciNetCrossRefGoogle Scholar
  67. 67.
    Xiao, L., Su, H., Chu, J.: Sliding mode prediction tracking control design for uncertain systems. Asian J. Control 9(3), 317–325 (2007)MathSciNetCrossRefGoogle Scholar
  68. 68.
    Xu, J.X., Abidi, K.: Discrete-time output integral sliding-mode control for a piezomotor-driven linear motion stage. IEEE Trans. Ind. Electron. 55(11), 3917–3926 (2008)CrossRefGoogle Scholar
  69. 69.
    Xu, Q.: Adaptive discrete-time sliding mode impedance control of a piezoelectric microgripper. IEEE Trans. Robot. 29(3), 663–673 (2013)CrossRefGoogle Scholar
  70. 70.
    Xu, Q., Jia, M.: Model reference adaptive control with perturbation estimation for a micropositioning system. IEEE Trans. Control Syst. Technol. 22(1), 352–359 (2014)MathSciNetCrossRefGoogle Scholar
  71. 71.
    Xu, Q., Li, Y.: Dahl model-based hysteresis compensation and precise positioning control of an XY parallel micromanipulator with piezoelectric actuation. J. Dyn. Syst. Meas. Control-Trans. ASME 132(4), 041011 (2010)Google Scholar
  72. 72.
    Xu, Q., Li, Y.: Micro-/nanopositioning using model predictive output integral discrete sliding mode control. IEEE Trans. Ind. Electron. 59(2), 1161–1170 (2012)CrossRefGoogle Scholar
  73. 73.
    Xu, Q., Li, Y.: Model predictive discrete-time sliding mode control of a nanopositioning piezostage without modeling hysteresis. IEEE Trans. Control Syst. Technol. 20(4), 983–994 (2012)CrossRefGoogle Scholar
  74. 74.
    Xu, Q., Wong, P.K.: Hysteresis modeling and compensation of a piezostage using least squares support vector machines. Mechatronics 21(7), 1239–1251 (2011)CrossRefGoogle Scholar
  75. 75.
    Yi, J., Chang, S., Shen, Y.: Disturbance observer-based hysteresis compensation for piezoelectric actuators. IEEE/ASME Trans. Mechatron. 14(4), 456–464 (2009)CrossRefGoogle Scholar
  76. 76.
    Yu, S., Alici, G., Shirinzadeh, B., Smith, J.: Sliding mode control of a piezoelectric actuator with neural network compensating rate-dependent hysteresis. In: Proceeding of IEEE International Conference on Robotics and Automation, pp. 3641–3645 (2005)Google Scholar
  77. 77.
    Yu, X., Kaynak, O.: Sliding mode control with soft computing: A survey. IEEE Trans. Ind. Electron. 56(9), 3275–3285 (2009)CrossRefGoogle Scholar
  78. 78.
    Yu, Y., Xiao, Z., Naganathan, N.G., Dukkipati, R.V.: Dynamic Preisach modelling of hysteresis for the piezoceramic actuator system. Mech. Mach. Theory 37(1), 75–89 (2002)MathSciNetCrossRefzbMATHGoogle Scholar

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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Electromechanical EngineeringUniversity of MacauMacauChina
  2. 2.Department of Electrical and Computer EngineeringNational University of SingaporeSingaporeSingapore

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