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Article

Fuzzy Adaptive Control for a 4-DOF Hand Rehabilitation Robot

1
CESTER-Research Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj-Napoca, Romania
2
European University of Technology, European Union
3
Neurology I Department, Cluj-Napoca Emergency Clinical County Hospital, 400012 Cluj-Napoca, Romania
4
Neurology Department, University of Medicine and Pharmacy “Iuliu Hatieganu”, 400012 Cluj-Napoca, Romania
5
Department of Neurology, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania
6
Department of Neurophysiology, National Center for Spinal Disorders, 1126 Budapest, Hungary
7
MEtRICs Research Centre, School of Engineering, University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal
8
Technical Sciences Academy of Romania, B-dul Dacia, 26, 030167 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Actuators 2025, 14(7), 351; https://doi.org/10.3390/act14070351
Submission received: 6 May 2025 / Revised: 14 July 2025 / Accepted: 16 July 2025 / Published: 17 July 2025

Abstract

This paper presents the development of a fuzzy-PID control able to adapt to several robot–patient interaction modes by monitoring patient evolution during the rehabilitation procedure. This control system is designed to provide targeted rehabilitation therapy through three interaction modes: passive; active–assistive; and resistive. By integrating a fuzzy inference system into the classical PID architecture, the FPID controller dynamically adjusts control gains in response to tracking error and patient effort. The simulation results indicate that, in passive mode, the FPID controller achieves a 32% lower RMSE, reduced overshoot, and a faster settling time compared to the conventional PID. In the active–assistive mode, the FPID demonstrates enhanced responsiveness and reduced error lag when tracking a sinusoidal reference, while in resistive mode, it more effectively compensates for imposed load disturbances. A rehabilitation scenario simulating repeated motion cycles on a healthy subject further confirms that the FPID controller consistently produces a lower overall RMSE and variability.
Keywords: hand rehabilitation robot; fuzzy-PID controller; interaction modes; kinematic modeling hand rehabilitation robot; fuzzy-PID controller; interaction modes; kinematic modeling

Share and Cite

MDPI and ACS Style

Tucan, P.; Vanta, O.-M.; Vaida, C.; Ciupe, M.; Sebeni, D.; Pisla, A.; Stiole, S.; Lupu, D.; Major, Z.; Gherman, B.; et al. Fuzzy Adaptive Control for a 4-DOF Hand Rehabilitation Robot. Actuators 2025, 14, 351. https://doi.org/10.3390/act14070351

AMA Style

Tucan P, Vanta O-M, Vaida C, Ciupe M, Sebeni D, Pisla A, Stiole S, Lupu D, Major Z, Gherman B, et al. Fuzzy Adaptive Control for a 4-DOF Hand Rehabilitation Robot. Actuators. 2025; 14(7):351. https://doi.org/10.3390/act14070351

Chicago/Turabian Style

Tucan, Paul, Oana-Maria Vanta, Calin Vaida, Mihai Ciupe, Dragos Sebeni, Adrian Pisla, Simona Stiole, David Lupu, Zoltan Major, Bogdan Gherman, and et al. 2025. "Fuzzy Adaptive Control for a 4-DOF Hand Rehabilitation Robot" Actuators 14, no. 7: 351. https://doi.org/10.3390/act14070351

APA Style

Tucan, P., Vanta, O.-M., Vaida, C., Ciupe, M., Sebeni, D., Pisla, A., Stiole, S., Lupu, D., Major, Z., Gherman, B., Bulbucan, V., Zima, I., Machado, J., & Pisla, D. (2025). Fuzzy Adaptive Control for a 4-DOF Hand Rehabilitation Robot. Actuators, 14(7), 351. https://doi.org/10.3390/act14070351

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