Nested Sliding Surface in Robust Neuro Fuzzy Control for a Servomechanism System Regulation
DOI:
https://doi.org/10.61467/2007.1558.2027.v18i1.1517Keywords:
Nested, NeuroFuzy, Servomechanism, Control anidado, neurodifuso, servomecanismoAbstract
This paper describes the design of a robust neuro-fuzzy control system for regulating a servomechanism, it is based on the development of a nested sliding-mode approach to effectively regulate the system and reduce the chattering effect. The approach involves developing a nested sliding mode control with gain adjustment using adaptive Neuro-Fuzzy Inference. Furthermore, the servomechanism is driven by the voltage applied to a DC motor, and the controlled outputs are the angular position and velocity of the shaft. One key feature of this advanced intelligent control system is its capability to efficiently regulate the position and speed of the servomechanism in an exponential sense, even in the presence of dynamic uncertainties and external disturbances. To this end, in a first step, a conditional nested sliding mode control was developed to reduce the magnitude of chattering effect. However, the magnitude of this conditional controller produces chatering effects on the actuator. To reduce this issue, Adaptive Neuro-Fuzzy Inference System suggests adjusting the control gains of the sliding modes. So, the proposed control scheme helps reduce the magnitude of the chattering effect in the control signal. To validate the proposed control strategy’s effectiveness, a comparative study based on an error criterica study is presented, highlighting its potential on the control process.
Acknowledgments
Omar Samperio Vázquez, CVU 551623. I would like to express my gratitude to the Autonomous University of the State of Hidalgo for the opportunity to pursue a Doctorate in Automation and Control, and to SECIHTI for the scholarship they awarded me for this program.
Spanish-language metadata / Metadatos en español
Título en español:
Superficie deslizante anidada en el control neurodifuso robusto para la regulación de un sistema servomecanismo
Resumen:
Este artículo describe el diseño de un sistema de control neurodifuso robusto para la regulación de un servomecanismo, basado en el desarrollo de un enfoque de modos deslizantes anidados para regular eficazmente el sistema y reducir el efecto de chattering. El enfoque consiste en desarrollar un control por modos deslizantes anidados con ajuste de ganancias mediante un sistema de inferencia neurodifuso adaptativo. Asimismo, el servomecanismo es accionado mediante el voltaje aplicado a un motor de corriente continua (DC), mientras que las salidas controladas corresponden a la posición angular y la velocidad del eje.
Una característica fundamental de este avanzado sistema de control inteligente es su capacidad para regular eficientemente la posición y la velocidad del servomecanismo con convergencia exponencial, incluso en presencia de incertidumbres dinámicas y perturbaciones externas. Para ello, en una primera etapa se desarrolló un control condicional por modos deslizantes anidados con el propósito de reducir la magnitud del efecto de chattering. Sin embargo, la magnitud de la acción de este controlador condicional genera efectos de chattering en el actuador.
Para mitigar este problema, se propone utilizar un Sistema de Inferencia Neurodifuso Adaptativo (ANFIS) para ajustar las ganancias de control de los modos deslizantes. De este modo, el esquema de control propuesto contribuye a reducir la magnitud del efecto de chattering en la señal de control. Para validar la eficacia de la estrategia de control propuesta, se presenta un estudio comparativo basado en criterios de error, destacando su potencial para el proceso de control.
Palabras Claves:
Control anidado, neurodifuso, servomecanismo.
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References
Asar, M. F., Elawady, W. M., & Sarhan, A. M. (2019). ANFIS-based an adaptive continuous sliding-mode controller for robot manipulators in operational space. Multibody System Dynamics, 47(2), 95–115. https://doi.org/10.1007/s11044-019-09681-5
Aydın, M., & Yakut, O. (2023). Implementation of sliding surface moving ANFIS based sliding mode control to rotary inverted pendulum. Journal of the Institute of Science and Technology, 13(2), 1165–1175. https://doi.org/10.21597/jist.1168611
Azar, A. T., & Zhu, Q. (Eds.). (2015). Advances and applications in sliding mode control systems. Springer. https://doi.org/10.1007/978-3-319-11173-5
Chapman, S. J. (2012). Máquinas eléctricas (5.ª ed.). McGraw-Hill Interamericana.
Cheng, M., Zhou, J., Qian, W., Wang, B., Zhao, C., & Han, P. (2024). Advanced electrical motors and control strategies for high-quality servo systems—A comprehensive review. Chinese Journal of Electrical Engineering, 10(1), 63–85. https://doi.org/10.23919/CJEE.2023.000048
Cruz-Ortiz, D., Chairez, I., & Poznyak, A. (2022). Non-singular terminal sliding-mode control for a manipulator robot using a barrier Lyapunov function. ISA Transactions, 121, 268–283. https://doi.org/10.1016/j.isatra.2021.04.001
Ertugrul, M., & Kaynak, O. (2000). Neuro sliding mode control of robotic manipulators. Mechatronics, 10(1–2), 239–263. https://doi.org/10.1016/S0957-4158(99)00057-4
Feng, Z.-R., Sha, R.-Z., & Ren, Z.-G. (2022). A chattering-reduction sliding mode control algorithm for affine systems with input matrix uncertainty. IEEE Access, 10, 58982–58996. https://doi.org/10.1109/ACCESS.2022.3179580
Franklin, G. F., Powell, J. D., & Workman, M. L. (1998). Digital control of dynamic systems (3rd ed.). Addison-Wesley.
Fridman, L., Moreno, J. A., Bandyopadhyay, B., Kamal, S., & Chalanga, A. (2015). Continuous nested algorithms: The fifth generation of sliding mode controllers. In X. Yu & M. Ö. Efe (Eds.), Recent advances in sliding modes: From control to intelligent mechatronics (pp. 5–35). Springer. https://doi.org/10.1007/978-3-319-18290-2_2
Hamzah, M. K., Al-Azzawi, R. S., Al-Jodah, A., Humaidi, A. J., & Hasan, A. F. (2024). Fuzzy logic-based chattering reduction in sliding mode control of single-link robot using muscle-like actuator. ICIC Express Letters, 18(3), 271–283. https://doi.org/10.24507/icicel.18.03.271
Hu, S., Ren, X., Zheng, D., & Chen, Q. (2024). Neural-network-based robust adaptive synchronization and tracking control for multimotor driving servo systems. IEEE Transactions on Transportation Electrification, 10(4), 9618–9630. https://doi.org/10.1109/TTE.2024.3374749
Huerta-Avila, H., Loukianov, A. G., & Cañedo, J. (2007). Nested integral sliding modes of large scale power system. In Proceedings of the 46th IEEE Conference on Decision and Control (pp. 1993–1998). IEEE. https://doi.org/10.1109/CDC.2007.4434719
Jang, J.-S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. https://doi.org/10.1109/21.256541
Kang, Z., Lin, X., Shen, X., Liu, Z., Gao, Y., & Liu, J. (2025). Event-triggered generalised super-twisting sliding mode control for position tracking of PMSMs. IEEE Transactions on Industrial Informatics, 21(7), 5701–5711. https://doi.org/10.1109/TII.2025.3556086
Khalil, H. K. (2004). Lyapunov stability. In H. Unbehauen (Ed.), Control systems, robotics, and automation (Vol. 12). EOLSS Publishers.
Levant, A. (2003). Higher-order sliding modes, differentiation and output-feedback control. International Journal of Control, 76(9–10), 924–941. https://doi.org/10.1080/0020717031000099029
Li, X., Liu, S., Wan, S., & Hong, J. (2020). Active suppression of milling chatter based on LQR-ANFIS. The International Journal of Advanced Manufacturing Technology, 111, 2337–2347. https://doi.org/10.1007/s00170-020-06279-6
Li, Z., & Zhai, J. (2024). Fuzzy adaptive super-twisting sliding mode asymptotic tracking control of robotic manipulators. International Journal of Fuzzy Systems, 26(1), 34–43. https://doi.org/10.1007/s40815-023-01573-3
Lin, H., Liu, J., Shen, X., Leon, J. I., Vazquez, S., Marquez Alcaide, A., Wu, L., & Franquelo, L. G. (2022). Fuzzy sliding-mode control for three-level NPC AFE rectifiers: A chattering alleviation approach. IEEE Transactions on Power Electronics, 37(10), 11704–11715. https://doi.org/10.1109/TPEL.2022.3174064
Miranda-Villatoro, F. A., Castaños, F., & Brogliato, B. (2017). A set-valued nested sliding-mode controller. IFAC-PapersOnLine, 50(1), 2971–2976. https://doi.org/10.1016/j.ifacol.2017.08.662
Polyakov, A., & Fridman, L. (2014). Stability notions and Lyapunov functions for sliding mode control systems. Journal of the Franklin Institute, 351(4), 1831–1865. https://doi.org/10.1016/j.jfranklin.2014.01.002
Poznyak, A. S. (2021). Classical and analytical mechanics: Theory, applied examples, and practice. Elsevier. https://doi.org/10.1016/C2020-0-01741-X
Prieto, P. J., Cazarez-Castro, N. R., Aguilar, L. T., & Cardenas-Maciel, S. L. (2017). Chattering existence and attenuation in fuzzy-based sliding mode control. Engineering Applications of Artificial Intelligence, 61, 152–160. https://doi.org/10.1016/j.engappai.2017.03.005
Saihi, L., Berbaoui, B., & Bakou, Y. (2023). Sliding mode of second-order control based on super-twisting ANFIS algorithm of doubly fed induction generator in wind turbines systems with real variable speeds. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 47(2), 473–490. https://doi.org/10.1007/s40998-022-00576-4
Sami, I., Ullah, S., Basit, A., Ullah, N., & Ro, J. S. (2020). Integral super twisting sliding mode based sensorless predictive torque control of induction motor. IEEE Access, 8, 186740–186755. https://doi.org/10.1109/ACCESS.2020.3028845
Šekara, T. B., & Mataušek, M. R. (2010). Revisiting the Ziegler–Nichols process dynamics characterization. Journal of Process Control, 20(3), 360–363. https://doi.org/10.1016/j.jprocont.2009.08.004
Shtessel, Y., Edwards, C., Fridman, L., & Levant, A. (2014). Sliding mode control and observation. Birkhäuser. https://doi.org/10.1007/978-0-8176-4893-0
Utkin, V., & Lee, H. (2006). Chattering problem in sliding mode control systems. In 2006 International Workshop on Variable Structure Systems (pp. 346–350). IEEE. https://doi.org/10.1109/VSS.2006.1644542
Utkin, V., Poznyak, A., Orlov, Y. V., & Polyakov, A. (2020). Road map for sliding mode control design. Springer. https://doi.org/10.1007/978-3-030-41709-3
Wang, A., Feng, X., Liu, H., & Yao, M. (2024). Design of sliding mode controller for servo feed system based on generalized extended state observer with reinforcement learning. Scientific Reports, 14, Article 24976. https://doi.org/10.1038/s41598-024-75598-5
Wang, N., & Adeli, H. (2012). Algorithms for chattering reduction in system control. Journal of the Franklin Institute, 349(8), 2687–2703. https://doi.org/10.1016/j.jfranklin.2012.06.001
Wang, Z., & Fei, J. (2022). Fractional-order terminal sliding-mode control using self-evolving recurrent Chebyshev fuzzy neural network for MEMS gyroscope. IEEE Transactions on Fuzzy Systems, 30(7), 2747–2758. https://doi.org/10.1109/TFUZZ.2021.3094717
Wong, C.-C., Huang, B.-C., & Lai, H.-R. (2001). Genetic-based sliding mode fuzzy controller design. Tamkang Journal of Science and Engineering, 4(3), 165–172. https://doi.org/10.6180/jase.2001.4.3.03
Yang, J., Shang, C., Li, Y., Li, F., & Shen, Q. (2021). ANFIS construction with sparse data via group rule interpolation. IEEE Transactions on Cybernetics, 51(5), 2773–2786. https://doi.org/10.1109/TCYB.2019.2952267
Yareshe, F. T., Madebo, N. W., Abdissa, C. M., & Lemma, L. N. (2025). Trajectory tracking of fixed-wing UAV using ANFIS-based sliding mode controller. IEEE Access, 13, 61986–62003. https://doi.org/10.1109/ACCESS.2025.3557472
Yonezawa, H., Yonezawa, A., & Kajiwara, I. (2025). Experimental verification of model-free active damping system based on virtual controlled object and fuzzy sliding mode control. Mechanical Systems and Signal Processing, 224, Article 111961. https://doi.org/10.1016/j.ymssp.2024.111961
Yu, X., & Efe, M. Ö. (Eds.). (2015). Recent advances in sliding modes: From control to intelligent mechatronics. Springer. https://doi.org/10.1007/978-3-319-18290-2
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