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Model Reference Based Neural Controller for Transmission Line Inspection Robot
  • +2
  • Zehra KARAGÖZ,
  • Nazmi EKREN,
  • Mustafa ŞAHİN,
  • Uğur DEMİR,
  • Ahmet Fevzi BABA
Zehra KARAGÖZ
Milli Savunma Universitesi
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Nazmi EKREN
Marmara Universitesi Teknoloji Fakultesi
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Mustafa ŞAHİN
Saglik Bilimleri Universitesi
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Uğur DEMİR
Marmara Universitesi Teknoloji Fakultesi

Corresponding Author:udemir@marmara.edu.tr

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Ahmet Fevzi BABA
Marmara Universitesi Teknoloji Fakultesi
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Abstract

The regular inspection of the power transmission lines is essential for the uninterrupted transmission of electrical energy to demand points. This quickly requires actions with economically, efficiently, and safely. Therefore, the transmission line inspection robots are inevitable solution as an alternative to existing line inspection methods. This study present design and control of a transmission line inspection robot (I-Robot). Since the I-Robot exhibits nonlinear behaviour and has multiple inputs and multiple outputs, a model reference-based neural controller is determined to achieve nonlinear control. The robot design process consists of 4 stages which are kinematic modelling, dynamic modelling, actuator modelling and controller design. To meet inspection requirements, the conceptual design of the I-Robot is performed, and the kinematic model are calculated in terms of the transformation matrices. According to the design requirements and system constraints, the dynamic model of the I-Robot is created. To provide desired motions and trajectory tracking, the actuator models are determined. Then, the I-Robot is prototyped. According to the dynamics of joint, robot and constraints, the system identification is performed to create reference model. During the system identification, the logged data are used the train the reference model. Finally, the desired trajectory for the driving cycles is created by manual excitation of the I-Robot. During the manual excitation, the logged data are used to train the neural network-based controller. Eventually, the I-Robot is assessed under the test scenarios in term of the trajectory tracking performance as regression value and mean squared errors.
24 Apr 2024Submitted to Journal of Field Robotics
25 Apr 2024Submission Checks Completed
25 Apr 2024Assigned to Editor
25 Apr 2024Review(s) Completed, Editorial Evaluation Pending
07 Jul 20241st Revision Received
10 Jul 2024Submission Checks Completed
10 Jul 2024Assigned to Editor
10 Jul 2024Review(s) Completed, Editorial Evaluation Pending
15 Jul 2024Reviewer(s) Assigned
17 Aug 2024Editorial Decision: Revise Major
10 Sep 20242nd Revision Received
11 Sep 2024Submission Checks Completed
11 Sep 2024Assigned to Editor
11 Sep 2024Review(s) Completed, Editorial Evaluation Pending
12 Sep 2024Reviewer(s) Assigned
21 Sep 2024Editorial Decision: Accept