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

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...

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Vydané v:Journal of optimization theory and applications Ročník 198; číslo 2; s. 745 - 766
Hlavní autori: Sadeghnejad, Soroush, Khadivar, Farshad, Esfandiari, Mojtaba, Amirkhani, Golchehr, Moradi, Hamed, Farahmand, Farzam, Vossoughi, Gholamreza
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.08.2023
Springer Nature B.V
Predmet:
Algorithms
Applications of Mathematics
Calculus of Variations and Optimal Control; Optimization
Control algorithms
Control theory
Damping
Degrees of freedom
Dynamic models
Engineering
Environment models
Haptic interfaces
Haptics
Impedance
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Output feedback
Predictive control
Robot arms
Simulation
Simulators
Theory of Computation
Training simulators
Virtual environments
Model predictive control (MPC)
Virtual reality-based haptic system
Robust stability
Endoscopic sinus surgery (ESS) training simulator
Quasi-min–max algorithm
Linear parametric variable
ISSN:0022-3239, 1573-2878
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:0022-3239
1573-2878
DOI:10.1007/s10957-023-02241-0