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access icon free Modified fuzzy neural network control using sliding mode technique for power quality improvement system with experimental verification

  • Author(s): Shixi Hou 1, 2 ; Yundi Chu 1, 2 ; Juntao Fei 1, 2
    • View affiliations
  • Source: Volume 14, Issue 19, 21 December 2020, p. 3029 – 3037
    DOI:  10.1049/iet-cta.2020.0770 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received 30/06/2020, Accepted 06/10/2020, Revised 29/08/2020, Published 25/11/2020

This study mainly focuses on the development of a second-order sliding mode-based meta-cognitive fuzzy neural network (MCFNN) control for power quality improvement, whereby an MCFNN is employed to learn modelling uncertainties. First, a second-order sliding mode control (SOSMC) is designed to track reference current for active power filter. Then, an MCFNN system with accurate approximation capability is further investigated to learn the unknown parts in SOSMC. Different from the existing predefined structure approaches, only necessary data can be extracted to adjust the structure and parameters of the networks in MCFNN. Subsequently, the Lyapunov stability analysis is presented to guarantee tracking performance and stability of the closed-loop system. Moreover, the excellent performance of the proposed MCFNN scheme is verified by simulation and experimental studies, and its remarkable characteristics are exhibited in comparison with other intelligent control schemes.

Inspec keywords: fuzzy neural nets; variable structure systems; power filters; control system synthesis; active filters; Lyapunov methods; adaptive control; neurocontrollers; uncertain systems; power supply quality; stability; learning (artificial intelligence)

Other keywords: active power filter; power quality improvement system; MCFNN system; Lyapunov stability analysis; mode technique; MCFNN scheme; modified fuzzy neural network control; intelligent control schemes; closed-loop system; second-order sliding mode-based meta-cognitive fuzzy neural network control; SOSMC

Subjects: Multivariable control systems; Self-adjusting control systems; Power supply quality and harmonics; Neurocontrol; Active filters and other active networks; Control of electric power systems; Other power apparatus and electric machines; Stability in control theory; Fuzzy control; Control system analysis and synthesis methods

References

    1. 1)
      • 25. Sun, N., Liang, D., Wu, Y., et al: ‘Adaptive control for pneumatic artificial muscle systems with parametric uncertainties and unidirectional input constraints’, IEEE Trans. Ind. Inf., 2020, 16, (2), pp. 969–979.
    2. 2)
      • 9. Yue, M., An, C., Ding, L., et al: ‘MPC motion planning-based sliding mode control for underactuated WPS vehicle via Olfati transformation’, IET Control Theory Appl., 2018, 12, (4), pp. 495–503.
    3. 3)
      • 32. Hou, S., Fei, J.: ‘A self-organizing global sliding mode control and its application to active power filter’, IEEE Trans. Power Electron., 2020, 35, (7), pp. 7640–7652.
    4. 4)
      • 34. Feng, Y., Zhou, M., Han, Q., et al: ‘Integral-type sliding-mode control for a class of mechatronic systems with gain adaptation’, IEEE Trans. Ind. Inf., 2020, 16, (8), pp. 5357–5368.
    5. 5)
      • 30. Rong, J., Yang, Z., Wong, P., et al: ‘A novel meta-cognitive fuzzy-neural model with backstepping strategy for adaptive control of uncertain nonlinear systems’, Neurocomputing, 2017, 230, pp. 332–344.
    6. 6)
      • 29. Wang, N., Meng, J.: ‘Self-constructing adaptive robust fuzzy neural tracking control of surface vehicles with uncertainties and unknown disturbances’, IEEE Trans. Control Syst. Technol., 2015, 23, (3), pp. 991–1002.
    7. 7)
      • 37. Escobedo-Alva, J.O., García-Estrada, E.C., Páramo-Carranza, L.A., et al: ‘Theoretical application of a hybrid observer on altitude tracking of quadrotor losing GPS signal’, IEEE Access, 2018, 6, pp. 76900–76908.
    8. 8)
      • 41. Saghafinia, A., Ping, H.W., Uddin, M.N., et al: ‘Adaptive fuzzy sliding-mode control into chattering-free IM drive’, IEEE Trans. Ind. App., 2015, 51, (1), pp. 692–701.
    9. 9)
      • 33. Hou, S., Fei, J., Chu, Y., et al: ‘Experimental investigation of adaptive fuzzy global sliding mode control of single-phase shunt active power filters’, IEEE Access, 2019, 7, pp. 64442–64449.
    10. 10)
      • 5. Ferreira, S.C., Gonzatti, R.B., Pereira, R.R., et al: ‘Finite control set model predictive control for dynamic reactive power compensation with hybrid active power filters’, IEEE Trans. Ind. Electron., 2018, 65, (3), pp. 2608–2617.
    11. 11)
      • 13. Nguyen, M.L., Chen, X.K., Yang, F.: ‘Discrete-time quasi-sliding mode control with prescribed performance function and its application topiezo-actuated positioning systems’, IEEE Trans. Ind. Electron., 2017, 65, (1), pp. 942–950.
    12. 12)
      • 27. Wang, J., Luo, W., Liu, J., et al: ‘Adaptive type-2 FNN-based dynamic sliding mode control of DC-DC boost converters’, IEEE Trans. Syst., Man, Cybern.,Syst., 2019, https://doi.org/10.1109/TSMC.2019.2911721.
    13. 13)
      • 22. Liu, X., Su, X., Shi, P., et al: ‘Observer-based sliding mode control for uncertain fuzzy systems via event-triggered strategy’, IEEE Trans. Fuzzy Syst., 2019, 27, (11), pp. 2190–2201.
    14. 14)
      • 2. Leal Serafim Rodrigues, P., Brandão Jacobina, C., de Freitas, N.B.: ‘Single-phase universal active power filter based on ac–dc–ac converter with eight controlled switches’, IET Power Electron., 2019, 12, (5), pp. 1131–1140.
    15. 15)
      • 36. Aguilar-Ibanez, C., Suarez-Castanon, M. S.: ‘A trajectory planning based controller to regulate an uncertain 3D overhead crane system’, Int. J. Appl. Math. Comput. Sci., 2019, 29, (4), pp. 693–702.
    16. 16)
      • 21. Hou, S., Fei, J., Chen, C., et al: ‘Finite-time adaptive fuzzy-neural-network control of active power filter’, IEEE Trans. Power Electron., 2019, 34, (10), pp. 10298–10313.
    17. 17)
      • 7. Thao, N.G.M., Uchida, K., Kofuji, K., et al: ‘An automatic-tuning scheme based on fuzzy logic for active power filter in wind farms’, IEEE Trans. Control Syst. Technol., 2019, 27, (4), pp. 1694–1702.
    18. 18)
      • 17. Lin, F., Hung, Y., Ruan, K.: ‘An intelligent second-order sliding-mode control for an electric power steering system using a wavelet fuzzy neural network’, IEEE Trans. Fuzzy Syst., 2014, 22, (6), pp. 1598–1611.
    19. 19)
      • 38. Hou, S., Fei, J.: ‘Adaptive fuzzy backstepping control of three-phase active power filter’, Control Eng. Practice, 2015, 45, pp. 12–21.
    20. 20)
      • 28. Wai, R., Yao, J., Lee, J.: ‘Backstepping fuzzy-neural-network control design for hybrid maglev transportation system’, IEEE Trans. Neural Netw. Learn. Syst., 2015, 26, (2), pp. 302–317.
    21. 21)
      • 6. Singh, Y., Hussain, I., Singh, B., et al: ‘Single-phase solar grid-interfaced system with active filtering using adaptive linear combiner filter-based control scheme’, IET Gener. Transm. Distrib., 2017, 11, (8), pp. 1976–1984.
    22. 22)
      • 20. Hou, S., Chu, Y., Fei, J.: ‘Intelligent global sliding mode control using recurrent feature selection neural network for active power filter’, IEEE Trans. Ind. Electron., 2020, https://doi.org/10.1109/TIE.2020.3000098.
    23. 23)
      • 26. Yang, T., Sun, N., Chen, H., et al: ‘Neural network-based adaptive antiswing control of an underactuated ship-mounted crane with roll motions and input dead zones’, IEEE Trans. Neural Netw. Learn. Syst., 2020, 31, (3), pp. 901–914.
    24. 24)
      • 3. Xiang, Z., Pang, Y., Wang, L., et al: ‘Design, control and comparative analysis of an LCLC coupling hybrid active power filter’, IET Power Electron., 2020, 13, (6), pp. 1207–1217.
    25. 25)
      • 14. Rubio, J.D.J., Martinez, D.I., Garcia, V., et al: ‘The perturbations estimation in two gas plants’, IEEE Access, 2020, 8, pp. 83081–83091.
    26. 26)
      • 39. Fei, J., Chen, Y.: ‘Dynamic terminal sliding-mode control for single-phase active power filter using new feedback recurrent neural network’, IEEE Trans. Power Electron., 2020, 35, (9), pp. 9904–9922.
    27. 27)
      • 19. Chu, Y., Fei, J., Hou, S.: ‘Adaptive global sliding-mode control for dynamic systems using double hidden layer recurrent neural network structure’, IEEE Trans. Neural Netw. Learn. Syst., 2020, 31, (4), pp. 1297–1309.
    28. 28)
      • 16. Rubio, J.D.J., Ochoa, G., Mujica-vargas, D., et al: ‘Structure regulator for the perturbations attenuation in a quadrotor’, IEEE Access, 2019, 7, pp. 138244–138252.
    29. 29)
      • 31. Subramanian, K., Suresh, S.: ‘A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system’, Appl. Soft. Comput., 2012, 12, (11), pp. 3603–3614.
    30. 30)
      • 15. Martinez, D.I., Rubio, J.D.J., Vargas, T.M., et al: ‘Stabilization of robots with a regulator containing the sigmoid mapping’, IEEE Access, 2020, 8, pp. 89479–89488.
    31. 31)
      • 40. Fei, J., Wang, H.: ‘Experimental investigation of recurrent neural network fractional-order sliding mode control of active power filter’, IEEE Trans. Circuits-II, 2020, 67, (11), pp. 2522–2526.
    32. 32)
      • 23. Wang, Y., Karimi, H. R., Lam, H., et al: ‘Fuzzy output tracking control and filtering for nonlinear discrete-time descriptor systems under unreliable communication links’, IEEE Trans. Cybern., 2020, 50, (6), pp. 2369–2379.
    33. 33)
      • 18. Fei, J., Chu, Y.: ‘Double hidden layer output feedback neural adaptive global sliding mode control of active power filter’, IEEE Trans. Power Electron., 2020, 35, (3), pp. 3069–3084.
    34. 34)
      • 11. García-Sánchez, J.R., Tavera-Mosqueda, S., Silva-Ortigoza, R., et al: ‘Robust switched tracking control for wheeled Mobile robots considering the actuators and drivers’, Sensors, 2018, 18, p. 4316.
    35. 35)
      • 4. Yang, L., Yang, J.: ‘A robust dual-loop current control method with a delay-compensation control link for LCL-type shunt active power filters’, IEEE Trans. Power Electron., 2019, 34, (7), pp. 6183–6199.
    36. 36)
      • 12. Li, G., Wang, X., Li, S.: ‘Consensus control of higher-order Lipschitz non-linear multi-agent systems based on backstepping method’, IET Control Theory Appl., 2020, 14, (3), pp. 490–498.
    37. 37)
      • 24. Dong, S., Chen, C.L.P., Fang, M., et al: ‘Dissipativity-based asynchronous fuzzy sliding mode control for T-S fuzzy hidden markov jump systems’, IEEE Trans. Cybern., 2020, 50, (9), pp. 4020–4030.
    38. 38)
      • 1. Durna, E.: ‘Adaptive fuzzy hysteresis band current control for reducing switching losses of hybrid active power filter’, IET Power Electron., 2018, 11, (5), pp. 937–944.
    39. 39)
      • 10. Wu, F., Li, P., Wang, J.: ‘FO improved fast terminal sliding mode control method for permanent-magnet synchronous motor with FO disturbance observer’, IET Control Theory Appl., 2019, 13, (10), pp. 1425–1434.
    40. 40)
      • 35. Yu, Z., Zhang, Y., Liu, Z., et al: ‘Distributed adaptive fractional-order fault-tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks’, IET Control Theory Appl., 2019, 13, (17), pp. 2917–2929.
    41. 41)
      • 8. Biricik, S., Komurcugil, H.: ‘Optimized sliding mode control to maximize existence region for single-phase dynamic voltage restorers’, IEEE Trans. Ind. Inf., 2016, 12, (4), pp. 1486–1497.
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