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A novel compound fast fractional integral sliding mode control and adaptive PI control of a MEMS gyroscope

  • Mehran Rahmani
  • Mohammad Habibur Rahman
Technical Paper
  • 12 Downloads

Abstract

This study considers a novel compound fast fractional integral sliding mode control and adaptive PI control (APIFFOISMC) of a MEMS gyroscope. MEMS gyroscope has been constantly encountered with external noises such as temperature change, vibration, and shock, which a new control law should be designed in order to be robust against mentioned perturbations. A novel fast fractional integral sliding mode control (FFOISMC) are proposed, which can suppress external disturbances. The main drawbacks of FFOISMC is creating a chattering phenomenon. Therefore, by using an adaptive PI controller, a novel compound control method is designed. An adaptive PI controller is able to continuously calculate an error value and applies correction value and then eliminates the chattering phenomenon. Simulation results illustrate the effectiveness of the proposed control technique.

Notes

References

  1. Ammour AS, Djennoune S, Aggoune W, Bettayeb M (2015) Stabilization of fractional-order linear systems with state and input delay. Asian J Control 17(5):1946–1954MathSciNetCrossRefGoogle Scholar
  2. Batur C, Sreeramreddy T, Khasawneh Q (2006) Sliding mode control of a simulated MEMS gyroscope. ISA Trans 45(1):99–108CrossRefGoogle Scholar
  3. Bourouba B, Ladaci S (2018) Robust fuzzy adaptive sliding mode stabilization for fractional-order chaos. Algorithms 11(7):101CrossRefGoogle Scholar
  4. Delavari H, Ghaderi R, Ranjbar A, Momani S (2010) Fuzzy fractional order sliding mode controller for nonlinear systems. Commun Nonlinear Sci Numer Simul 15(4):963–978MathSciNetCrossRefGoogle Scholar
  5. Delavari H, Lanusse P, Sabatier J (2013) Fractional order controller design for a flexible link manipulator robot. Asian J Control 15(3):783–795MathSciNetCrossRefGoogle Scholar
  6. Diao Z, Quan H, Lan L, Han Y (2013) Analysis and compensation of mems gyroscope drift. In: 2013 Seventh international conference on sensing technology (ICST). IEEE, pp 592–596Google Scholar
  7. Duc TM, Van Hoa N, Dao TP (2018) Adaptive fuzzy fractional-order nonsingular terminal sliding mode control for a class of second-order nonlinear systems. J Comput Nonlinear Dyn 13(3):031004CrossRefGoogle Scholar
  8. Jakovljević B, Pisano A, Rapaić MR, Usai E (2016) On the sliding-mode control of fractional-order nonlinear uncertain dynamics. Int J Robust Nonlinear Control 26(4):782–798MathSciNetCrossRefGoogle Scholar
  9. Jin L, Gao Q, Hou Y, Sun Z, Jia L, Li K (2015) Fractional neural sliding mode control for the electro-hydraulic servo system. In: Advanced information technology, electronic and automation control conference (IAEAC), 2015 IEEE. IEEE, pp 810–815Google Scholar
  10. Khettab K, Bensafia Y, Ladaci S (2017) Chattering elimination in fuzzy sliding mode control of fractional chaotic systems using a fractional adaptive proportional integral controller. Int J Intell Eng Syst 10(5):255–265Google Scholar
  11. Liu N, Fei J (2017) Adaptive fractional sliding mode control of active power filter based on dual RBF neural networks. IEEE Access 5:27590–27598CrossRefGoogle Scholar
  12. Luo J, Liu H (2014) Adaptive fractional fuzzy sliding mode control for multivariable nonlinear systems. Discrete Dyn Nat Soc 2014(6):1–10.  https://doi.org/10.1155/2014/541918 MathSciNetCrossRefGoogle Scholar
  13. Noureddine B, Djamel B, Boudjema F (2013) Tuning fuzzy fractional order PID sliding-mode controller using PSO algorithm for nonlinear systems. In: 2013 3rd international conference on systems and control (ICSC). IEEE, pp 797–803Google Scholar
  14. Rahmani M (2018) MEMS gyroscope control using a novel compound robust control. ISA Trans 72:37–43CrossRefGoogle Scholar
  15. Rahmani M, Rahman MH (2018) New robust control of a 7-DOF exoskeleton robot. PLos One.  https://doi.org/10.1371/journal.pone.0203440 CrossRefGoogle Scholar
  16. Rahmani M, Ghanbari A, Ettefagh MM (2016a) Hybrid neural network fraction integral terminal sliding mode control of an Inchworm robot manipulator. Mech Syst Signal Process 80:117–136CrossRefGoogle Scholar
  17. Rahmani M, Ghanbari A, Ettefagh MM (2016b) Robust adaptive control of a bio-inspired robot manipulator using bat algorithm. Expert Syst Appl 56:164–176CrossRefGoogle Scholar
  18. Rahmani M, Ghanbari A, Ettefagh MM (2018a) A novel adaptive neural network integral sliding-mode control of a biped robot using bat algorithm. J Vib Control 24(10):2045–2060MathSciNetCrossRefGoogle Scholar
  19. Rahmani M, Komijani H, Ghanbari A, Ettefagh MM (2018b) Optimal novel super-twisting PID sliding mode control of a MEMS gyroscope based on multi-objective bat algorithm. Microsyst Technol 24(6):2835–2846CrossRefGoogle Scholar
  20. Shi S (2017) Extended disturbance observer based sliding mode control for fractional-order systems. In control conference (CCC), 2017 36th Chinese. IEEE, pp 11385–11389Google Scholar
  21. Shirkavand M, Pourgholi M (2018) Robust fixed-time synchronization of fractional order chaotic using free chattering nonsingular adaptive fractional sliding mode controller design. Chaos Solitons Fractals 113:135–147MathSciNetCrossRefGoogle Scholar
  22. Sun G, Ma Z (2017) Practical tracking control of linear motor with adaptive fractional order terminal sliding mode control. IEEE/ASME Trans Mechatron 22(6):2643–2653CrossRefGoogle Scholar
  23. Talebi J, Ganjefar S (2018) Fractional order sliding mode controller design for large scale variable speed wind turbine for power optimization. Environ Prog Sustain Energy 37(6):2124–2131CrossRefGoogle Scholar
  24. Ullah N, Han S, Khattak MI (2016) Adaptive fuzzy fractional-order sliding mode controller for a class of dynamical systems with uncertainty. Trans Inst Meas Control 38(4):402–413CrossRefGoogle Scholar
  25. Wang LM (2017) Model-free adaptive sliding mode controller design for generalized projective synchronization of the fractional-order chaotic system via radial basis function neural networks. Pramana 89(3):38CrossRefGoogle Scholar
  26. Xiong L, Wang J, Mi X, Khan MW (2018) Fractional order sliding mode based direct power control of grid-connected DFIG. IEEE Trans Power Syst 33(3):3087–3096CrossRefGoogle Scholar
  27. Yan W, Hou S, Fang Y, Fei J (2017) Robust adaptive nonsingular terminal sliding mode control of MEMS gyroscope using fuzzy-neural-network compensator. Int J Mach Learn Cybern 8(4):1287–1299CrossRefGoogle Scholar
  28. Yang B, Yu T, Shu H, Zhu D, An N, Sang Y, Jiang L (2018) Perturbation observer based fractional-order sliding-mode controller for MPPT of grid-connected PV inverters: design and real-time implementation. Control Eng Pract 79:105–125CrossRefGoogle Scholar
  29. Zhao G, Li H, Song Z (2013) Adaptive dynamic fuzzy neural network-based decoupled sliding-mode controller with hybrid sliding surfaces. Int J Autom Control 7(3):183–201CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentUniversity of Wisconsin-MilwaukeeMilwaukeeUSA

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