Design and Kinematics of a 5-DOF Parallel Robot for Beating Heart Surgery

Ersoy, Onur; Yildirim, Mehmet C.; Ahmad, Awais; Yirmibeşoğlu, Osman Doğan; Koroglu, Nazli; Bebek, Özkan

doi:10.1109/icarm.2019.8833632

Cited by 1 publication

(3 citation statements)

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“…Where 𝑠 = [𝑠 1 𝑠 2 … 𝑠 𝑘 ] 𝑇 , 𝜁 𝑖 = 𝑑𝑖𝑎𝑔(𝜁 𝑖 1 , 𝜁 𝑖 2 , … , 𝜁 𝑖 𝑘 ) with 𝑖 = 1, 2, 3, … , 𝑛. 𝜁 𝑖 is found to satistfy the Hurwitz polynomial. Differentiating 𝑠 (𝑒(𝑡)) with respect to time leads to equation (6).…”

Section: Dynamic Equation Descriptionmentioning

confidence: 99%

“…𝒔̇(𝒆(𝑡)) = 𝒆 (𝑛) (𝑡) + 𝜻 1 𝒆 (𝑛−1) (𝑡) + ⋯ + 𝜻 𝑛 𝒆(𝑡) = 𝑪 𝑇 𝒆̇(𝑡) + 𝑲 𝑇 𝒆(𝑡) (6) Where 𝐶 = [0 0 … 𝐼] 𝑇 and 𝐾 = [𝜁 𝑛 𝜁 𝑛−1 … 𝜁 1 ] 𝑇 denotes the feedback gain matrix.…”

Section: Dynamic Equation Descriptionmentioning

confidence: 99%

“…The ruggedness, durability, and small size of parallel robots have led to many applications in medical fields, manufacturing, transportation... [1][2][3]. However, understanding the mathematical model of a parallel robot is a complex challenge because the issues related to the weight and the moment of inertia at each joint are difficult to determine [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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An Application of Modified T2FHC Algorithm in Two-Link Robot Controller

Ngo,

Tran,

et al. 2023

Journal of Robotics and Control

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Parallel robotic systems have shown their advantages over the traditional serial robots such as high payload capacity, high speed, and high precision. Their applications are widespread from transportation to manufacturing fields. Therefore, most of the recent studies in parallel robots focus on finding the best method to improve the system accuracy. Enhancing this metric, however, is still the biggest challenge in controlling a parallel robot owing to the complex mathematical model of the system. In this paper, we present a novel solution to this problem with a Type 2 Fuzzy Coherent Controller Network (T2FHC), which is composed of a Type 2 Cerebellar Model Coupling Controller (CMAC) with its fast convergence ability and a Brain Emotional Learning Controller (BELC) using the Lyaponov-based weight updating rule. In addition, the T2FHC is combined with a surface generator to increase the system flexibility. To evaluate its applicability in real life, the proposed controller was tested on a Quanser 2-DOF robot system in three case studies: no load, 180 g load and 360 g load, respectively. The results showed that the proposed structure achieved superior performance compared to those of available algorithms such as CMAC and Novel Self-Organizing Fuzzy CMAC (NSOF CMAC). The Root Mean Square Error (RMSE) index of the system that was 2.20E-06 for angle A and 2.26E-06 for angle B and the tracking error that was -6.42E-04 for angle A and 2.27E-04 for angle B demonstrate the good stability and high accuracy of the proposed T2FHC. With this outstanding achievement, the proposed method is promising to be applied to many applications using nonlinear systems.

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Section: Dynamic Equation Descriptionmentioning

confidence: 99%

Section: Dynamic Equation Descriptionmentioning

confidence: 99%