• KHADIDJA MAKHLOUFI Laboratoire des énergies renouvelables et les réseaux intelligents, Université Tahri Mohamed of Bechar
  • SIDAHMED ZEGNOUNE Laboratoire des énergies renouvelables et les réseaux intelligents, Université Tahri Mohamed of Bechar
  • AYMAN OMARI Laboratoire des énergies renouvelables et les réseaux intelligents, Université Tahri Mohamed of Bechar
  • ISMAIL KHALIL BOUSSERHANE Laboratoire des énergies renouvelables et les réseaux intelligents, Université Tahri Mohamed of Bechar; ARHIPEL Laboratory, University Tahri Mohamed of Bechar


Linear permanent magnet synchronous machine, Sliding mode, Fuzzy-sliding, Neural networks, Adaptive neuro-fuzzy inference systems


In this paper, position tuning of permanent magnet linear synchronous machine (MSAPL) using neuro-fuzzy-based adaptive sliding mode controller (ANFIS) was proposed. First, the vector control of the MSAPL was derived. Subsequently, an adaptive fuzzy-sliding controller was designed for the position adjustment of the MSAPL. A fuzzy adapter is used to dynamically adjust the parameters of the discontinuous part 'sat'. The control signal obtained by the FSMC presents sudden variations due to the chattering phenomenon. Finally, to eliminate chatter and improve performance, an adaptive neuro-fuzzy system has been proposed for adapting the parameters of the fuzzy-sliding controller. The control scheme developed is verified by a numerical simulation. The simulation results of the adaptive slippery neuro-fuzzy controller showed good tuning performance compared to the slippery and slippery-fuzzy modes, and the chattering was significantly reduced.


(1) R. Ghislain, Commande optimisée d’un actionneur linéaire synchrone pour un axe de positionnement rapide, Thèse de doctorat, École Nationale Supérieure d’Arts et Métiers de Paris, (2007).

(2) F.J. Lin, S.Y. Chen, L.T. Teng, A. Hen, Recurrent functional-link-based fuzzy neural network controller with improved particle swarm optimization for a linear synchronous motor drive, IEEE Transactions on Magnetics, 45, 8, pp. 3151–3165, (2009).

(3) A. khlaief, Contribution à la commande vectorielle sans capteur mécanique des machines synchrones à aimants permanents, Thèse Doctorat, Université d’Aix-Marseille, 2012.

(4) J. Slotine and W. Li, Applied Nonlinear Control, Prentice-Hall, Englewood Cliffs, NJ, 1991.

(5) V. I. Utkin and A. S. Poznyak, Adaptive sliding mode control, Chapter 2, Lecture Notes in Control and Information Sciences, 440, pp. 21–53 (2013).

(6) W.S. Lin, C.S. Chen, Robust adaptive sliding mode control using fuzzy modelling for a class of uncertain MIMO nonlinear systems, IEE Proceedings - Control Theory and Applications, 149, 3, pp. 193–201 (2002).

(7) A. Larbaoui, B. Belabbes, A. Meroufel, D. Bouguenna: Commande par mode glissant floue de la machine synchrone, Rev. Roum. Sci. Techn.– Électrotechn. et Énerg. 62, 2, pp. 192–196 (2017).

(8) A. Boucheta, I. K. Bousserhane, A. Hazzab, B. Mazari, M. K. Fellah: Fuzzy-sliding mode controller for linear induction motor control, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 54, 4, pp. 405–414 (2009).

(9) A. Mechernene, M. Loucif, M. Zerikat: Induction motor control based on a fuzzy sliding mode approach, Rev. Roum. Sci. Techn.– Électrotechn. et Énerg. 64, 1, pp. 39–44 (2019).

(10) A. Kerboua, M. Abid, Hybrid fuzzy sliding mode control of a doubly-fed induction generator in wind turbines, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 57, 4, pp. 412–421 (2009)

(11) I. Senol, M. Demirtas, S. Rustemov, B. Gumus, Position control of induction motor: a new-bounded fuzzy sliding mode controller, International Journal for Computation Mathematics in Electrical and Electronic Engineering (COMPEL), 24, 1, pp. 145–157 (2005).

(12) F.J. Lin, D.H. Wang, P.K. Huang, FPGA-based fuzzy sliding-mode control for a linear induction motor drive, IEE Proceeding on Electric Power Application, 152, 5, pp. 1137–1148 (2005)

(13) R.J. Wai, Y.C. Huang, Z.W. Yang, C.Y. Shih, Adaptive fuzzy-neural-network velocity sensorless control for robot manipulator position tracking, IET Control Theory Applications., 4, 6, pp. 1079–1093 (2010).

(14) W. Wei, W. YuHua, W. ShiRong, A speed control system of permanent magnet linear synchronous motor using neuron adaptive controller, Second International Conference on Intelligent Computation Technology and Automation, pp. 35–38 (2009).

(15) J. Zhao, X. Zhang, J. Zhang, PMLSM recurrent neural network compensation simulation study, IPEMC, pp. 1832–1835 (2009).

(16) F.J. Lin, R.J. Wai, C.M. Hong, Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs, IEEE Transactions on Neural Networks, 12, 1, pp. 68–90 (2001).

(17) Y.S. Kung, Design and implementation of a high-performance PMLSM drives using DSP chip, IEEE Transactions on Industrial Electronics, 55, 3, pp. 1341–1351 (2008).

(18) Y.S. Kung, C.C. Huang, M.H. Tsai, FPGA realization of an adaptive fuzzy controller for PMLSM drive, IEEE Transactions on Industrial Electronics, 56, 8, pp. 2923–2932 (2009).

(19) J.S.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics, 23, 3, pp. 665–684 (1993).

(20) Y.S. Kung, N.K. Quang, L.T.V.Anh, FPGA-based neural fuzzy controller design for PMLSM drive, International Conference on Power Electronics and Drive Systems (PEDS), pp. 222–227 (2009).






Électrotechnique et électroénergétique / Electrical and Power Engineering