On the Dynamic Calibration and Trajectory Control of Arash:Asist Publisher



Hassani A¹ ; Dindarloo MR¹ ; Khorrambakht R¹ ; Bataleblu A¹ ; Heidari R¹ ; Motaharifar M² ; Mohammadi SF³ ; Taghirad HD¹ Show Affiliations
Source: 2022 8th International Conference on Control# Instrumentation and Automation# ICCIA 2022 Published:2022

Abstract

This article investigates the dynamic parameter calibration of ARAS Haptic System for EYE Surgery Training (ARASH:ASiST). ARASH:ASiST is a 3-DOF haptic device developed for intraocular surgery training. In this paper, the linear regression form of the dynamic formulation of the system with respect to its dynamic parameters is derived. Then the dynamic parameters of ARASH:ASiST are calibrated using the least square (LS) identification scheme. The cross-validation results for different trajectories indicate that the identified model has a suitable approximate fitness percentage in both translational and rotational motions of the surgical instrument. Finally, a robust model-based controller is implemented on the real prototype by the use of the calibration outcome, and it is verified that by using the estimated dynamic model, the trajectory tracking performance is significantly improved and the tracking error is reduced 50% compared to that of using the nominal dynamic model of the robot. © 2022 IEEE.