Hardware implementation of AI-based fuzzy logic for real-time sign language interpreter robotic control using FPGA
By: Kshitij Shinghal, Amit Saxena, Avnesh Verma and Rajul Misra
References
- L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338-353, 1965. doi: 10.1016/S0019-9958(65)90241-X.
- O. K. Tonguz and G. Ferrari, Ad Hoc Wireless Networks: A Communication-Theoretic Perspective, 1st ed. Wiley, 2006. doi: 10.1002/0470091126.
- J. M. Mendel, “Type-2 fuzzy sets and systems: An overview,” IEEE Computational Intelligence Magazine, vol. 2, no. 1, pp. 20-29, 2007. doi: 10.1109/MCI.2007.380672.
- S. Sánchez-Solano, A. J. Cabrera, I. Baturone, F. J. Moreno-Velo, and M. Brox, “FPGA implementation of embedded fuzzy controllers for robotic applications,” IEEE Transactions on Industrial Electronics, vol. 54, no. 4, pp. 1937-1945, 2007. doi: 10.1109/TIE.2007.898292.
- A. Verma, S. Dhingra, and M. K. Soni, “VLSI-cell placement technique for architecture of field programmable gate array (FPGA) design,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 17, no. 3, 2009. doi: 10.3906/elk-0908-173.
- A. Verma, S. Dhingra, and M. K. Soni, “Design and synthesis of FPGA for speed control of induction motor,” International Journal of Physical Sciences, vol. 4, no. 11, pp. 645-650, 2009. doi: 10.5897/IJPS.9000155.
- D. N. Oliveira, A. P. de Souza Braga, and O. d. M. Almeida, “Fuzzy logic controller implementation on a FPGA using VHDL,” in Proc. 2010 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), Toronto, ON, Canada, 2010, pp. 1-6. doi: 10.1109/NAFIPS.2010.5548192.
- M. S. Anand and B. Tyagi, “Design and implementation of fuzzy controller on FPGA,” International Journal of Intelligent Systems and Applications, vol. 4, no. 10, pp. 35-42, 2012. doi: 10.5815/ijisa.2012.10.04.
- R. Rani, M. Verma, and A. Verma, “Application of fuzzy inference system for interpreting physical fitness metrics of male and female trainee athletes: De-fuzzification through hull and sigma scale analysis,” Journal of Electronic & Information Systems, vol. 7, no. 2, pp. 13-24, 2025. doi: 10.30564/jeis.v7i2.10609.
- A. Verma and V. A. Tiwari, “A fuzzy logic controller to minimize the effect of input temperature fluctuations on the deviation of relative humidity to avoid instrument’s deterioration,” International Journal of Research in Advent Technology, vol. 7, no. 6, 2019. doi: 10.32622/ijrat.76201948.
- A. Verma and V. A. Tiwari, “Optimum fuzzy based approach to improve the instrument’s performance affected by environmental conditions,” Serbian Journal of Electrical Engineering, vol. 10, no. 2, pp. 309-318, 2013. doi: 10.2298/SJEE121219006V.
- E. A. Ramadan, M. El-Bardini, and M. A. Fkirin, “Design and FPGA-implementation of an improved adaptive fuzzy logic controller for DC motor speed control,”Ain Shams Engineering Journal, vol. 5, no. 3, pp. 803-816, 2014. doi: 10.1016/j.asej.2014.04.002.
- I. Baturone, A. Gersnoviez, and Á. Barriga, “Neuro-fuzzy techniques to optimize an FPGA-embedded controller for robot navigation,” Applied Soft Computing, vol. 21, pp. 95-106, 2014. doi: 10.1016/j.asoc.2014.03.001.
- T. Tămas and S. Brassai, “Hardware implementation of a neuro-fuzzy controller using high level synthesis tool,” MACRo 2015, 2015. doi: 10.1515/macro-2015-0018.
- C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1-9. doi: 10.1109/CVPR.2015.7298594.
- I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
- A. Alabdo, J. Pérez, G. J. Garcia, J. Pomares, and F. Torres, “FPGA-based architecture for direct visual control robotic systems,” Mechatronics, vol. 39, pp. 204-216, 2016. doi: 10.1016/j.mechatronics.2016.05.008.
- A. Tandon, A. Saxena, K. Mehrotra, K. Kashyap, and H. Kaur, “A review paper on smart glove - converts Indian Sign Language (ISL) into text and speech,” International Journal for Scientific Research & Development, vol. 4, no. 8, 2016.
- H. Kaur, A. Saxena, A. Tandon, K. Mehrotra, and K. Kashyap, “A review paper on evolution of smart glove,” International Journal of Scientific Research and Management Studies, vol. 3, no. 3, pp. 124-128, 2016.
- K. Mehrotra, A. Saxena, K. Kashyap, H. Kaur, and A. Tandon, “Augmentative and alternative communication using smart glove,” International Journal of Computer Engineering & Science, vol. 5, no. 3, pp. 41-50, 2017.
- K. Cpałka, Design of Interpretable Fuzzy Controllers. Cham, Switzerland: Springer International Publishing, 2017. doi: 10.1007/978-3-319-52881-6.
- H.-C. Huang, C.-W. Tao, C.-C. Chuang, and J.-J. Xu, “FPGA-based mechatronic design and real-time fuzzy control with computational intelligence optimization for omni-Mecanum-wheeled autonomous vehicles,” Electronics, vol. 8, no. 11, p. 1328, 2019. doi: 10.3390/electronics8111328.
- J. Ruiz-Rosero, G. Ramirez-Gonzalez, and R. Khanna, “Field programmable gate array applications —A scientometric review,” Computation, vol. 7, no. 4, p. 63, 2019. doi: 10.3390/computation7040063.
- C. Li, J. Chen, and M. Jiang, “Research on application of embedded single chip computer intelligent control,” in Proc. 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), Harbin, China, 2020, pp. 588-591. doi: 10.1109/ICMCCE51767.2020.00132.
- E. Noorsal, M. S. M. Aras, M. H. Baharudin, M. R. Ghazali, and M. S. M. Aras, “Design of an FPGA-based fuzzy feedback controller,” Micromachines, vol. 12, no. 8, p. 968, 2021. doi: 10.3390/mi12080968.
- E. Aridhi, D. Popescu, and A. Mami, “FPGA based co-design of a speed fuzzy logic controller applied to an autonomous car,” International Journal of Reconfigurable and Embedded Systems, vol. 10, no. 3, pp. 195-211, 2021. doi: 10.11591/ijres.v10.i3.pp195-211.
- A. A. Nada and M. A. Bayoumi, “Development of embedded fuzzy control using reconfigurable FPGA technology,” Automatika, vol. 65, no. 2, pp. 609-626, 2024. doi: 10.1080/00051144.2024.2313904.
- A. Chopde, S. Joshi, S. Surve, and S. Chavanke, “FPGA based real-time hand gesture recognition system,” in Proc. 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India, 2024, pp. 1-6. doi: 10.1109/ICCCNT61001.2024.10724416.
- A. Fekik, H. Khati, A. T. Azar, M. L. Hamida, H. Denoun, I. A. Hameed, N. A. Kamal, and others, “FPGA in the loop implementation of the PUMA 560 robot based on backstepping control,” IET Control Theory & Applications, vol. 18, pp. 1877-1891, 2024. doi: 10.1049/cth2.12589.
- Y. Lin, L. Wang, Z. Zhang, and X. Liu, “A hardware-accelerated federated learning with intuitionistic fuzzy security measures for AI adaptive image computing,” IEEE Access, vol. 13, pp. 198358-198372, 2025. doi: 10.1109/ACCESS.2025.3633060.
- R. Niwas, M. Verma, and A. Verma, “A soft computing-based decision support system for multidimensional assessment of non-linear coordinative performance in combat sport athletes using a fuzzy inference system,” Physical Education and Sport Through the Centuries, vol. 13, no. 1, pp. 113-128, 2026. doi: 10.5937/spes2601113N.
- E. Aridhi, K. Laabidi, and A. Mami, “FPGA technology in healthcare: A comprehensive review of hardware and software solutions for diagnostics, imaging, and patient care,” Array, vol. 28, p. 100622, 2025. doi: 10.1016/j.array.2025.100622.
- H. Tanaka and Y. Kobayashi, “FPGA implementation of a PID and fuzzy hybrid controller for robotic arm stabilization,” International Journal of Research in Advanced Electronics Engineering, vol. 6, no. 2, pp. 33-37, 2025.
- C. Li, H. Jiao, W. Huang, Y. Huang, and F. Liu, “An FPGA-based quantization and acceleration framework for Multi-DNN,” in Proc. 2025 IEEE International Symposium on Circuits and Systems (ISCAS), London, U.K., 2025, pp. 1-5. doi: 10.1109/ISCAS56072.2025.11044304.
- M. Y. Novopoltsev, A. Tulenkov, R. Murtazin, R. I. Akhidov, I. Zemtsova, E. E. Bojarskaja, D. N. Bondarenko, A. V. Savchenko, and I. Makarov, “From data to signs: A foundation model for multilingual sign language recognition,” IEEE Access, vol. 13, pp. 188170-188181, 2025. doi: 10.1109/ACCESS.2025.3627130.
- M. A. Touat, H. Khati, A. Fekik, A. T. Azar, H. Talem, R. Mellah, and S. Ahmed, “FPGA design and implementation of fuzzy learning control: Application on DC motor position control,” An International Journal of Optimization and Control: Theories & Applications, vol. 15, no. 3, pp. 435-448, 2025. doi: 10.36922/IJOCTA025070023.
- M. H. Sabzalian, C. Du, and A. Mohammadzadeh, “A camera-based type-3 fuzzy formation control of multiple robots,” Results in Control and Optimization, vol. 21, p. 100633, 2025. doi: 10.1016/j.rico.2025.100633.
- A. Saday and İ. A. Özkan, “FPGA-based adaptive fuzzy speed control of welding robots with image processing techniques,” Welding in the World, 2025. doi: 10.1007/s40194-025-02153-9.
- A. M. Z. Khaki and A. Choi, “Real-time and resource-efficient embedded computer vision via optimizing lightweight CNNs for FPGA acceleration,” Journal of Signal Processing Systems, vol. 97, pp. 185-195, 2025. doi: 10.1007/s11265-025-01966-7.
- M. R. Kale, C. Kaur, I. Kumar, H. S. Mahdi, V. Uprikar, and S. V., “Development of an Arabic sign language recognition system utilizing deep convolutional neural network,” in Proc. Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 2025, pp. 1-6. doi: 10.1109/ICAECT63952.2025.10958963.
- H. Khati, A. Fekik, M. A. Touat, A. T. Azar, H. Gritli, H. Talem, and R. Mellah, “Hardware implementation of a neuro-fuzzy controller for robotic manipulators,” in Advances in Nonlinear Dynamical Systems and Robotics (ANDC): Modeling, Dynamics and Control Approaches for Modern Robotics. Academic Press, 2026, pp. 249-277. doi: 10.1016/B978-0-443-30106-3.00011-7.
- A. K. Nagarajan, K. Pandiyan, B. Manimaran, P. Nagarajan, and N. Swetha, “FPGA implementation of image registration using accelerated CNN,” in Proc. 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS), Chennai, India, 2023, pp. 1-7. doi: 10.1109/ICCEBS58601.2023.10448526.
- M. Ji, Z. Al-Ars, P. Hofstee, Y. Chang, and B. Zhang, “FPQNet: Fully pipelined and quantized CNN for ultra-low latency image classification on FPGAs using OpenCAPI,” Electronics, vol. 12, no. 19, p. 4085, 2023. doi: 10.3390/electronics12194085.
- L. Gao, Z. Luo, and L. Wang, “Convolutional neural network acceleration techniques based on FPGA platforms: Principles, methods, and challenges,” Information, vol. 16, no. 10, p. 914, 2025. doi: 10.3390/info16100914.
- Y. Xu, J. Luo, and W. Sun, “Flare: An FPGA-based full precision low power CNN accelerator with reconfigurable structure,” Sensors, vol. 24, no. 7, p. 2239, 2024. doi: 10.3390/s24072239.
- Z. An, K. Peng, K. Yang, C. Lin, L. Zeng, and W. Yang, “An efficient parallel computing CNN accelerator based on FPGA hardware resources,” SSRN, 2024. doi: 10.2139/ssrn.4861407.
DOI: https://doi.org/10.2478/jee-2026-0049 | Journal eISSN: 1339-309X (formerly 1335-3632) | Journal ISSN: 1335-3632
Language: English
Page range: 516 - 528
Submitted on: Jun 20, 2026
Published online: Oct 11, 2026
Published in print: Oct 1, 2026
Published by: Slovak University of Technology in Bratislava
In partnership with: Paradigm Publishing Services
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© 2026 Kshitij Shinghal, Amit Saxena, Avnesh Verma, Rajul Misra, published by Slovak University of Technology in Bratislava
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.