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access icon openaccess Novel product ANFIS-PID hybrid controller for buck converters

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 10/02/2018, Accepted 30/04/2018, Revised 08/04/2018, Published 11/07/2018

This study introduces the use of arithmetic and logical hybrid controllers between proportional–integrative–derivative (PID) controllers and adaptive neuro-fuzzy inference system (ANFIS) for voltage regulation with buck-type DC–DC converters and proposes the novel product hybrid controller. The product hybrid controller combines the advantages of a PID controller and ANFIS controller to obtain an improved response and light and heavy load efficiency for the buck converter. PIDs are known for their good response and robustness but suffer due to non-optimal tuning for non-linear systems. ANFISs, on the other hand, excel in varying and abnormal conditions. ANFIS controllers also can improve and adapt their response to the current load or inputs with time. Simulation results are presented and analysed for all the controllers to validate the controller designs. The controllers are also verified experimentally. It is observed that the hybrid controllers provide enhanced tracking and response capabilities in comparison to classical PID controllers, with the novel product hybrid improving on the steady-state error, peak efficiency, and overall light and load operation.

Inspec keywords: fuzzy reasoning; control system synthesis; DC-DC power convertors; voltage regulators; neurocontrollers; fuzzy control; voltage control; three-term control; adaptive control

Other keywords: classical PID controllers; ANFIS controller; ANFIS-PID hybrid controller; controller designs; buck-type DC-DC converters; proportional-integrative-derivative controllers; adaptive neuro-fuzzy inference system; nonlinear systems; nonoptimal tuning; novel product hybrid controller; logical hybrid controllers; PID controller; heavy load efficiency; arithmetic hybrid controllers; buck converter

Subjects: Reasoning and inference in AI (theory); Fuzzy control; Self-adjusting control systems; Voltage control; Control of electric power systems; DC-DC power convertors; Neurocontrol

References

    1. 1)
      • 19. Singh, M., Chandra, A.: ‘Real-time implementation of ANFIS control for renewable interfacing inverter in 3P4W distribution network’, IEEE Trans. Ind. Electron., 2013, 60, (1), pp. 121–128.
    2. 2)
      • 4. Utomo, W.M., Bakar, A., Ahmad, M., et al: ‘Online learning neural network control of buck-boost converter’. Eighth Int. Conf. of Information Technology: New Generations (ITNG), Las Vegas, NV, 2011, pp. 485–489.
    3. 3)
      • 26. Ang, K.H., Chong, G., Li, Y.: ‘PID control system analysis, design, and technology’, IEEE Trans. Control Syst. Technol., 2005, 13, (4), pp. 559–576.
    4. 4)
      • 6. Oucheriah, S., Guo, L.: ‘PWM-based adaptive sliding-mode control for boost DC–DC converters’, IEEE Trans. Ind. Electron., 2013, 60, (8), pp. 3291–3294.
    5. 5)
      • 23. Allaoua, B., Gasbaoui, B., Mebarki, B.: ‘Setting up PID DC motor speed control alteration parameters using particle swarm optimization strategy’, Leonardo Electron. J. Pract. Technol., 2009, 14, pp. 19–32.
    6. 6)
      • 11. Vinod, S., Balaji, M., Prabhakar, M.: ‘Robust control of parallel buck fed buck converter using hybrid fuzzy PI controller’. IEEE 11th Int. Conf. on Power Electronics and Drive Systems (PEDS), Sydney, NSW, 2015, pp. 347–351.
    7. 7)
      • 8. Hidayat, H., Hadi, S.P., Sarjiya, S., et al: ‘Performance analysis of hybrid PID-ANFIS for speed control of brushless DC motor base on identification model system’, Int. J. Comput. Inf. Technol., 2013, 2, (4), pp. 1–7.
    8. 8)
      • 20. Abu-Rub, H., Iqbal, A., Ahmed, S.M., et al: ‘Quasi-Z-source inverter-based photovoltaic generation system with maximum power tracking control using ANFIS’, IEEE Trans. Sustain. Energy, 2013, 4, (1), pp. 11–20.
    9. 9)
      • 17. Dounis, A.I., Kofinas, P., Alafodimos, C., et al: ‘Adaptive fuzzy gain scheduling PID controller for maximum power point tracking of photovoltaic system’, Renew. Energy, 2013, 60, pp. 202–214.
    10. 10)
      • 15. Elshaer, M., Mohamed, A., Mohammed, O.A.: ‘Smart optimal control of DC-DC boost converter for intelligent PV systems’. 16th Int. Conf. on Intelligent System Application to Power Systems (ISAP), Hersonissos, 2011, pp. 1–6.
    11. 11)
      • 10. Sarvi, M., NamazyPour, N.: ‘A new PID-fuzzy controller for DC/DC converters’. 43rd Int. Universities Power Engineering Conf. (UPEC), Padova, 2008, pp. 1–4.
    12. 12)
      • 22. Copeland, B.R.: ‘The design of PID controllers using Ziegler Nichols tuning’, J. Intell. Robot. Syst., 2008, 11, (15), pp. 121–127.
    13. 13)
      • 14. Rubaai, A., Castro-Sitiriche, M.J., Ofoli, A.R.: ‘DSP-based laboratory implementation of hybrid fuzzy-PID controller using genetic optimization for high-performance motor drives’, IEEE Trans. Ind. Appl., 2008, 44, (6), pp. 1977–1986.
    14. 14)
      • 9. Shabaninia, F., Pouyanfar, S., Abbasi, S.H.: ‘Design and analysis of neuro fuzzy logic PD controller for PWM-based switching converter’, Universal J. Control Autom., 2014, 2, (2), pp. 58–64.
    15. 15)
      • 25. Elmas, C., Deperlioglu, O., Sayan, H.H.: ‘Adaptive fuzzy logic controller for DC–DC converters’, Expert Syst. Appl., 2009, 36, (2), pp. 1540–1548.
    16. 16)
      • 24. Jang, J.S.R., Sun, C.T., Mizutani, E.: ‘Fuzzy inference systems. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence’ (Prentice Hall, NJ, 1997), pp. 73–91.
    17. 17)
      • 5. Martinez-Salamero, L., Garcia, G., Orellana, M., et al: ‘Start-up control and voltage regulation in a boost converter under sliding-mode operation’, IEEE Trans. Ind. Electron., 2013, 60, (10), pp. 4637–4649.
    18. 18)
      • 21. Chang, C., Yuan, Y., Jiang, T., et al: ‘Field programmable gate array implementation of a single-input fuzzy proportional–integral–derivative controller for DC–DC buck converters’, IET Power Electron., 2016, 9, (6), pp. 1259–1266.
    19. 19)
      • 2. Minorsky, N.: ‘Directional stability of automatically steered bodies’, Nav. Eng. J., 1922, 34, (2), pp. 280-309.
    20. 20)
      • 18. Singh, D.: ‘Modeling and control of passenger body vibrations in active quarter car system: a hybrid ANFIS PID approach’, Int. J. Dyn. Control, 2018, pp. 1–14,DOI: 10.1007/s40435-018-0409-z.
    21. 21)
      • 3. Jang, J.S.: ‘ANFIS: adaptive-network-based fuzzy inference system’, IEEE Trans. Syst. Man Cybern., 1993, 23, (3), pp. 665–685.
    22. 22)
      • 13. Darvill, J., Tisan, A., Cirstea, M.: ‘An ANFIS-PI based boost converter control scheme’. IEEE 13th Int. Conf. on Industrial Informatics (INDIN), Cambridge, 2015, pp. 632–639.
    23. 23)
      • 16. Vindhya, V., Reddy, V.: ‘PID-Fuzzy logic hybrid controller for a digitally controlled DC-DC converter’. Int. Conf. on Green Computing, Communication and Conservation of Energy (ICGCE), Chennai, 2013, pp. 362–366.
    24. 24)
      • 12. Yu, H.C., Chen, T.C., Liu, C.S.: ‘Adaptive fuzzy logic proportional-integral-derivative control for a miniature autofocus voice coil motor actuator with retaining force’, IEEE Trans. Magn., 2014, 50, (11), pp. 1–4.
    25. 25)
      • 1. Wang, K., Rahman, N., Lukic, Z., et al: ‘All-digital DPWM/DPFM controller for low-power DC-DC converters’. Twenty-First Annual IEEE Applied Power Electronics Conf. and Exposition (APEC), Dallas, TX, 2006, pp. 719–723.
    26. 26)
      • 7. Pramonohadi, S.: ‘A comparative study of PID, ANFIS and hybrid PID-ANFIS controllers for speed control of brushless DC motor drive’. Int. Conf. on Computer, Control, Informatics and Its Applications (IC3INA), Jakarta, 2013, pp. 117–122.
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