Comparative Analysis of Model-Based and Data-Driven Control for Tendon-Driven Robotic Fingers

Kanat Suleimenov, Akim Kapsalyamov, Beibit Abdikenov, Aiman Ozhikenova, Yerbolat Igembay and Kassymbek Ozhikenov ()
Additional contact information
Kanat Suleimenov: Department of Information Technology and Entrepreneurship, Narva College, University of Tartu, 20307 Narva, Estonia
Akim Kapsalyamov: Faculty of Engineering and Mathematics, Hochschule Bielefeld, 33619 Bielefeld, Germany
Beibit Abdikenov: ReLive Research, Astana 010000, Kazakhstan
Aiman Ozhikenova: Institute of Automation and Information Technologies, Satbayev University, Almaty 050000, Kazakhstan
Yerbolat Igembay: Institute of Automation and Information Technologies, Satbayev University, Almaty 050000, Kazakhstan
Kassymbek Ozhikenov: Institute of Automation and Information Technologies, Satbayev University, Almaty 050000, Kazakhstan

Mathematics, 2025, vol. 13, issue 22, 1-26

Abstract: The control of tendon-driven robotic fingers presents significant challenges due to their inherent underactuation, coupled with complex non-linear dynamics arising from tendon elasticity, friction, and external disturbances. Therefore, achieving precise control of finger motion and contact interactions necessitates advanced modeling, estimation, and control strategies capable of addressing uncertainties in tendon tension, routing, and elasticity. This paper presents a comprehensive comparative study of three distinct control paradigms: feedback linearization with Proportional-Derivative (FBL-PD) control, feedback linearization with super-twisting sliding-mode algorithm (FBL-STA), and deep-deterministic reinforcement learning (DDPG-RL), for the precise trajectory tracking of a three-link tendon-driven robotic finger. Through extensive simulations, the performance of each controller is rigorously evaluated based on trajectory-tracking accuracy and robustness to varying disturbances. The results indicate that under disturbance-free conditions, the FBL-PD and FBL-STA controllers, when properly tuned, achieve precise tracking of the reference trajectory; however, they produce noticeably noisy control signals. When subjected to external disturbances, these controllers exhibit increased sensitivity, producing even noisier responses. In contrast, the DDPG-RL maintains smooth control dynamics and achieves sufficiently accurate tracking in both scenarios. This comparative analysis elucidates the strengths and weaknesses of each control strategy, offering critical insights and practical guidelines for the design and implementation of advanced control systems for dexterous tendon-driven robotic fingers.

Keywords: dynamic modelling; robotic finger; tendon-driven mechanism; prosthetic finger; underactuated control; reinforcement learning; feedback linearization; super-twisting sliding mode control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/22/3669/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/22/3669/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:22:p:3669-:d:1795628

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().