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Publication Date

Fall 2023

Degree Type

Thesis - Campus Access Only

Degree Name

Master of Science (MS)

Department

Mechanical Engineering

Advisor

Lin Jiang; Mojtaba Sharifi; Winncy Du

Abstract

Teleoperated robotics technologies are expected to expand rapidly, with applications in education, manufacturing, medical science, and space exploration. Robots can be controlled remotely using advanced control methods, allowing real-time interactions within physical environments and sufficient stabilization. The proposed work aims to track the designed path for a teleoperated robotic needling insertion system with adaptive fuzzy neural network modeling and control. The T-S fuzzy neural network model is employed to represent a nonlinear manipulator dynamic system. The proposed adaptive control aims to estimate the parameters of the dynamic system online and adapt to the end effector force and velocity uncertainties. The estimated parameters for the T-S fuzzy model's link, mass, and load will be used to compute the serve torque for each joint. Simulation studies will be used to illustrate the system's stability and tracking performance. The success of this work will facilitate the application of the teleoperated robotic system in a complex operation that requires stabilization and accuracy during the process.

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