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Using an Improved Output Feedback MPC Approach for Developing a Haptic Virtual Training System

Author

Listed:
  • Soroush Sadeghnejad

    (Amirkabir University of Technology (Tehran Polytechnic))

  • Farshad Khadivar

    (Sharif University of Technology)

  • Mojtaba Esfandiari

    (Sharif University of Technology)

  • Golchehr Amirkhani

    (Sharif University of Technology)

  • Hamed Moradi

    (Sharif University of Technology)

  • Farzam Farahmand

    (Sharif University of Technology)

  • Gholamreza Vossoughi

    (Sharif University of Technology)

Abstract

Haptic training simulators generally consist of three major components, namely a human operator, a haptic interface, and a virtual environment. Appropriate dynamic modeling of each of these components can have far-reaching implications for the whole system's performance improvement in terms of transparency, the analogy to the real environment, and stability. In this paper, we developed a virtual-based haptic training simulator for Endoscopic Sinus Surgery by doing a dynamic characterization of the phenomenological sinus tissue fracture in the virtual environment, using an input-constrained linear parametric variable model. A parallel robot manipulator equipped with a calibrated force sensor is employed as a haptic interface. A lumped five-parameter single-degree-of-freedom mass-stiffness-damping impedance model is assigned to the operator’s arm dynamic. A robust online output feedback quasi-min–max model predictive control framework is proposed to stabilize the system during the switching between the piecewise linear dynamics of the virtual environment. The simulations and the experimental results demonstrate the effectiveness of the proposed control algorithm in terms of robustness and convergence to the desired impedance quantities.

Suggested Citation

  • Soroush Sadeghnejad & Farshad Khadivar & Mojtaba Esfandiari & Golchehr Amirkhani & Hamed Moradi & Farzam Farahmand & Gholamreza Vossoughi, 2023. "Using an Improved Output Feedback MPC Approach for Developing a Haptic Virtual Training System," Journal of Optimization Theory and Applications, Springer, vol. 198(2), pages 745-766, August.
    • Handle: RePEc:spr:joptap:v:198:y:2023:i:2:d:10.1007_s10957-023-02241-0
    DOI: 10.1007/s10957-023-02241-0
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    References listed on IDEAS

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    1. S. M. Lee & S. C. Won & J. H. Park, 2008. "New Robust Model Predictive Control for Uncertain Systems with Input Constraints Using Relaxation Matrices," Journal of Optimization Theory and Applications, Springer, vol. 138(2), pages 221-234, August.
    2. S. M. Lee & O. M. Kwon & Ju H. Park, 2014. "Output Feedback Model Predictive Tracking Control Using a Slope Bounded Nonlinear Model," Journal of Optimization Theory and Applications, Springer, vol. 160(1), pages 239-254, January.
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