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Adaptive neuro-fuzzy inference system-based grey time-varying sliding mode control for power conditioning applications

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Abstract

This paper develops an adaptive neuro-fuzzy inference system-based grey time-varying sliding mode control (TVSMC) for the application of power conditioning systems. The presented methodology combines the merits of TVSMC, grey prediction (GP), and adaptive neuro-fuzzy inference system (ANFIS). Compared with classic sliding mode control, the TVSMC accelerates reaching phase and guarantees the sliding mode existence starting at arbitrary primary circumstance. But, as a highly nonlinear loading occurs, the TVSMC will undergo chattering and steady-state errors, thus degrading PCS’s performance. The GP is therefore used to attenuate the chattering if the overestimate of system uncertainty bounds exists and to lessen steady-state errors if the underestimate of system uncertainty bounds happens. Also, the GP-compensated TVSMC control gains are optimally tuned by the ANFIS for achieving more precise tracking. Using the proposed methodology, the power conditioning system (PCS) robustness is increased expectably, and low distorted output voltage and fast transient response at PCS output can be achieved even under nonlinear loading. The analysis in theory, design process, simulations, and digital signal processing-based experimental realization for PCS are represented to support the efficacy of the proposed methodology. Because the proposed methodology is easier to implement than prior methodologies and provides high tracking accuracy and low computational complexity, the contents of this paper will be of interest to learners of correlated artificial intelligence applications.

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References

  1. Shin HB, Park JG (2012) Anti-windup PID Controller with integral state predictor for variable-speed motor drives. IEEE Trans Ind Electron 59(3):1509–1516

    Article  Google Scholar 

  2. Rasoanarivo I, Brechet S, Battiston A, Nahid-Mobarakeh B (2012) Behavioral analysis of a boost converter with high performance source filter and a fractional-order PID controller. In: Proceedings of IEEE international conference on industry applications society annual meeting, pp 1–6

  3. Martin D, Santi E (2014) Autotuning of digital deadbeat current controllers for grid-tie inverters using wide bandwidth impedance identification. IEEE Trans Ind Appl 50(1):441–451

    Article  Google Scholar 

  4. Wang C, Ooi BT (2015) Incorporating deadbeat and low-frequency harmonic elimination in modular multilevel converters. IET Gener Transm Distrib 9(4):369–378

    Article  Google Scholar 

  5. Hu J, Zhu ZQ (2013) Improved voltage-vector sequences on dead-beat predictive direct power control of reversible three-phase grid-connected voltage-source converters. IEEE Trans Power Electron 28(1):254–267

    Article  Google Scholar 

  6. Chen Y, Luo A, Shuai Z, Xie S (2013) Robust predictive dual-loop control strategy with reactive power compensation for single-phase grid-connected distributed generation system. IET Power Electron 6(7):1320–1328

    Article  Google Scholar 

  7. Herran MA, Fischer JR, Gonzalez SA, Judewicz MG, Carugati I, Carrica DO (2014) Repetitive control with adaptive sampling frequency for wind power generation systems. IEEE J Emerg Select Top Power Electron 2(1):58–69

    Article  Google Scholar 

  8. Yan QZ, Wu XJ, Yuan XB, Geng YW (2016) An improved grid-voltage feedforward strategy for high-power three-phase grid-connected inverters based on the simplified repetitive predictor. IEEE Trans Power Electron 31(5):3880–3897

    Article  Google Scholar 

  9. Chen Z, Yao B, Wang Q (2015) Mu-synthesis-based adaptive robust control of linear motor driven stages with high-frequency dynamics: a case study. IEEE/ASME Trans Mechatron 20(3):1482–1490

    Article  Google Scholar 

  10. Bevrani H, Feizi MR, Ataee S (2016) Robust frequency control in an islanded microgrid: h-infinity and mu-synthesis approaches. IEEE Trans Smart Grid 7(2):706–717

    Google Scholar 

  11. Shtessel Y, Edwards C, Fridman L, Levant A (2014) Sliding mode control and observation. Springer, New York

    Book  Google Scholar 

  12. Liu JK, Wang XH (2012) Advanced sliding mode control for mechanical systems design, analysis and MATLAB simulation. Springer, Heidelberg

    MATH  Google Scholar 

  13. Gudey SK, Gupta R (2015) Reduced state feedback sliding-mode current control for voltage source inverter-based higher-order circuit. IET Power Electron 8(8):1367–1376

    Article  Google Scholar 

  14. Rezaei MM, Soltani J (2015) Robust control of an islanded multi-bus microgrid based on input–output feedback linearisation and sliding mode control. IET Gener Transm Distrib 9(15):2447–2454

    Article  Google Scholar 

  15. Kang SW, Kim KH (2015) Sliding mode harmonic compensation strategy for power quality improvement of a grid-connected inverter under distorted grid condition. IET Power Electron 8(8):1461–1472

    Article  Google Scholar 

  16. Vidal-Idiarte E, Marcos-Pastor A, Garcia G, Cid-Pastor A, Martinez-Salamero L (2015) Discrete-time sliding-mode-based digital pulse width modulation control of a boost converter. IET Power Electron 8(5):708–714

    Article  Google Scholar 

  17. Montoya DG, Ramos-Paja CA, Giral R (2016) Improved design of sliding-mode controllers based on the requirements of MPPT techniques. IEEE Trans Power Electron 31(1):235–247

    Article  Google Scholar 

  18. More JJ, Puleston PF, Kunusch C, Fantova MA (2015) Development and implementation of a supervisor strategy and siding mode control setup for fuel-cell-based hybrid generation systems. IEEE Trans Energy Convers 30(1):218–225

    Article  Google Scholar 

  19. Tan SC, Lai YM, Tse CK (2012) Sliding mode control of switching power converters: techniques and implementation. CRC Press, Boca Raton

    Google Scholar 

  20. Azar AT, Zhu QM (2015) Advances and applications in sliding mode control systems. Springer, Heidelberg

    Book  MATH  Google Scholar 

  21. Geng J, Sheng YZ, Liu XD (2013) Second-order time-varying sliding mode control for reentry vehicle. Int J Intell Comput Cybern 6(3):272–295

    Article  MathSciNet  Google Scholar 

  22. Li L, Zhang QZ, Rasol N (2011) Time-varying sliding mode adaptive control for rotary drilling system. J Comput 6(3):564–570

    Google Scholar 

  23. Liu S, Lin Y (2010) Advances in grey systems research. Springer, Heidelberg

    Book  Google Scholar 

  24. Chang GW, Lu HJ (2012) Forecasting flicker severity by grey predictor. IEEE Trans Power Deliv 27(4):2428–2430

    Article  Google Scholar 

  25. Saremi S, Mirjalili SZ, Mirjalili SM (2015) Evolutionary population dynamics and grey wolf optimizer. Neural Comput Appl 26(5):1257–1263

    Article  Google Scholar 

  26. Ren WH, Lu ZM (2016) Link importance evaluation based on gray relational analysis for communication networks. J Netw Intell 1(1):38–45

    Google Scholar 

  27. Kass E, Eden T, Brown N (2014) Analysis of neural data. Springer, New York

    Book  MATH  Google Scholar 

  28. Syropoulos A (2014) Theory of fuzzy computation. Springer, New York

    Book  MATH  Google Scholar 

  29. Wang GY, Guan BL (2015) Fuzzy adaptive variational Bayesian unscented Kalman filter. J Inf Hiding Multimed Signal Process 6(4):740–749

    Google Scholar 

  30. Chen SM, Chang YC, Pan JS (2013) Fuzzy rules interpolation for sparse fuzzy rule-based systems based on interval type-2 Gaussian fuzzy sets and genetic algorithms. IEEE Trans Fuzzy Syst 21(3):412–425

    Article  Google Scholar 

  31. JessiSahayaShanthi L, Arumugam R, Taly YK (2012) A novel rotor position estimation approach for a 8/6 solid rotor switched reluctance motor. Neural Comput Appl 21(3):461–468

    Article  Google Scholar 

  32. Uddin MN, Huang ZR, Hossain AB (2014) Development and implementation of a simplified self-tuned neuro–fuzzy-based IM drive. IEEE Trans Ind Appl 50(1):51–59

    Article  Google Scholar 

  33. Chairez I (2013) Differential neuro-fuzzy controller for uncertain non-linear systems. IEEE Trans Fuzzy Syst 21(2):369–384

    Article  Google Scholar 

  34. Jang JSR, Sun CT, Mizutani E (2015) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Pearson Education, New Delhi

    Google Scholar 

  35. Rahmani R, Langeroudi NMA, Yousefi R, Mahdian M, Seyedmahmoudian M (2014) Fuzzy logic controller and cascade inverter for direct torque control of IM. Neural Comput Appl 25(3–4):879–888

    Article  Google Scholar 

  36. Wang XX, Ma LY (2014) A compact K nearest neighbor classification for power plant fault diagnosis. J Inf Hiding Multimed Signal Process 5(3):508–517

    Google Scholar 

  37. Tsai PW, Alsaedi A, Hayat T, Chen CW (2016) A novel control algorithm for interaction between surface waves and a permeable floating structure. China Ocean Eng 30(2):161–176

    Article  Google Scholar 

  38. Lin CM, Leng CH, Hsu CF, Chen CH (2009) Robust neural network control system design for linear ultrasonic motor. Neural Comput Appl 18(6):567–575

    Article  Google Scholar 

  39. Tsai PW, Chen CW (2014) A novel criterion for nonlinear time-delay systems using LMI fuzzy Lyapunov method. Appl Soft Comput 25:461–472

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Ministry of Science and Technology of Taiwan, R.O.C., under contract number MOST104-2221-E-214-011.

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Authors and Affiliations

  1. Department of Electrical Engineering, I-Shou University, No.1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung City, 84001, Taiwan, ROC

    En-Chih Chang, Rong-Ching Wu & Guan-Yu Chen

  2. Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Pokfulam, Hong Kong

    Ke ZHU

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  1. En-Chih Chang
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  2. Rong-Ching Wu
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  3. Ke ZHU
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  4. Guan-Yu Chen
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Correspondence to En-Chih Chang.

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Chang, EC., Wu, RC., ZHU, K. et al. Adaptive neuro-fuzzy inference system-based grey time-varying sliding mode control for power conditioning applications. Neural Comput & Applic 30, 699–707 (2018). https://doi.org/10.1007/s00521-016-2515-1

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  • DOI: https://doi.org/10.1007/s00521-016-2515-1

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