Noise Control Techniques

  • Aydin AziziEmail author
  • Poorya Ghafoorpoor Yazdi
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


In the scientific terminology, noise control is an operation which involves filtering, canceling, or reducing out the unwanted noise or interference from the signal contaminated by noise so that the desired signal can be recovered.This chapter aims to introduce noise control techniques focusing on utilizing sliding mode and PID controllers to reduce the effect of noise on mechanical structures. 


  1. 1.
    M.R. Anbiyaei, White Noise Reduction for Wideband Sensor Array Signal Processing (University of Sheffield, 2018)Google Scholar
  2. 2.
    S. Manikandan, Literature survey of active noise control systems. Acad. Open Internet J. 17 (2006)Google Scholar
  3. 3.
    A. Azizi, Computer-based analysis of the stochastic stability of mechanical structures driven by white and colored noise. Sustainability 10(10), 3419 (2018)CrossRefGoogle Scholar
  4. 4.
    A. Ashkzari, A. Azizi, Introducing genetic algorithm as an intelligent optimization technique, in Applied Mechanics and Materials, vol. 568. (Trans Tech Publ, 2014), pp. 793–797Google Scholar
  5. 5.
    A. Azizi, Introducing a novel hybrid artificial intelligence algorithm to optimize network of industrial applications in modern manufacturing. Complexity 2017 (2017)MathSciNetCrossRefGoogle Scholar
  6. 6.
    A. Azizi, Hybrid artificial intelligence optimization technique, in Applications of Artificial Intelligence Techniques in Industry 4.0 (Springer, 2019), pp. 27–47Google Scholar
  7. 7.
    A. Azizi, Modern manufacturing, in Applications of Artificial Intelligence Techniques in Industry 4.0 (Springer, 2019), pp. 7–17Google Scholar
  8. 8.
    A. Azizi, RFID network planning, in Applications of Artificial Intelligence Techniques in Industry 4.0 (Springer, 2019), pp. 19–25Google Scholar
  9. 9.
    A. Azizi, Applications of Artificial Intelligence Techniques in Industry 4.0 (ed: Springer)Google Scholar
  10. 10.
    A. Azizi, F. Entesari, K.G. Osgouie, M. Cheragh, Intelligent mobile robot navigation in an uncertain dynamic environment, in Applied Mechanics and Materials, vol. 367. (Trans Tech Publ, 2013), pp. 388–392Google Scholar
  11. 11.
    A. Azizi, F. Entessari, K.G. Osgouie, A.R. Rashnoodi, Introducing neural networks as a computational intelligent technique, in Applied Mechanics and Materials, vol. 464. (Trans Tech Publ, 2014), pp. 369–374Google Scholar
  12. 12.
    A. Azizi, N. Seifipour, Modeling of Dermal Wound Healing-Remodeling Phase by Neural Networks, in International Association of Computer Science and Information Technology-Spring Conference, 2009, IACSITSC’09 (IEEE, 2009) pp. 447–450Google Scholar
  13. 13.
    A. Azizi, A. Vatankhah Barenji, M. Hashmipour, Optimizing radio frequency identification network planning through ring probabilistic logic neurons. Adv. Mech. Eng. 8(8), p. 1687814016663476 (2016)CrossRefGoogle Scholar
  14. 14.
    A. Azizi, P.G. Yazdi, M. Hashemipour, Interactive design of storage unit utilizing virtual reality and ergonomic framework for production optimization in manufacturing industry. Int. J. Interac. Des. Manuf. (IJIDeM) 1–9 (2018)Google Scholar
  15. 15.
    M. Koopialipoor, A. Fallah, D.J. Armaghani, A. Azizi, E.T. Mohamad, Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Eng. Comput. 1–14 (2018)Google Scholar
  16. 16.
    K.G. Osgouie, A. Azizi, Optimizing Fuzzy Logic Controller for Diabetes Type I by Genetic Algorithm. in The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010, vol. 2. (IEEE, 2010), pp. 4–8Google Scholar
  17. 17.
    S. Rashidnejhad, A.H. Asfia, K.G. Osgouie, A. Meghdari, A. Azizi, Optimal trajectory planning for parallel robots considering time-jerk, in Applied Mechanics and Materials, vol. 390. (Trans Tech Publ, 2013), pp. 471–477Google Scholar
  18. 18.
    S. Elliott, Signal Processing for Active Control (Elsevier, 2000)Google Scholar
  19. 19.
    P.A. Nelson, S.J. Elliott, Active noise control: a tutorial review. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 75(11), 1541–1554 (1992)Google Scholar
  20. 20.
    S.V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction (Wiley, 2008)Google Scholar
  21. 21.
    S.C. Douglas, Introduction to adaptive filters, in Digital Signal Processing Handbook (1999), pp. 7–12Google Scholar
  22. 22.
    S. Das, K.K. Sarma, Noise cancellation in stochastic wireless channels using coding and adaptive filtering. Communicated Int. J. Comput. Appl. (IJCA), 2012Google Scholar
  23. 23.
    S.O. Haykin, Adaptive Filter Theory (Pearson Higher Ed, 2013)Google Scholar
  24. 24.
    F.R. Jiménez-López, C.E. Pardo-Beainy, E.A. Gutiérrez-Cáceres, Adaptive filtering implemented over TMS320c6713 DSP platform for system identification. Iteckne 11(2), 157–171 (2014)CrossRefGoogle Scholar
  25. 25.
    R.K. Ravi, FPGA Implementation of Adaptive Filter Architectures (2012)Google Scholar
  26. 26.
    M. Liu, C. Suh, Simultaneous time-frequency control of friction-induced instability. J. Appl. Nonlinear Dyn. 3(3), 227–243 (2014)CrossRefGoogle Scholar
  27. 27.
    A.P. Vinod, E.M.-K. Lai, in Design of Low Complexity High-Speed Pulse-Shaping IIR Filters for Mobile Communication Receivers. IEEE International Symposium on Circuits and Systems, 2005, ISCAS 2005 (IEEE, 2005), pp. 352–355Google Scholar
  28. 28.
    B. Friedlander, Lattice filters for adaptive processing. Proc. IEEE 70(8), 829–867 (1982)CrossRefGoogle Scholar
  29. 29.
    V.J. Mathews, Adaptive polynomial filters. IEEE Signal Process. Mag. 8(3), 10–26 (1991)CrossRefGoogle Scholar
  30. 30.
    S.M. Kuo, D.R. Morgan, in Review of DSP Algorithms for Active Noise Control. Proceedings of the 2000 IEEE International Conference on Control Applications (IEEE, 2000), pp. 243–248Google Scholar
  31. 31.
    S.M. Kuo, D.R. Morgan, Active noise control: a tutorial review. Proc. IEEE 87(6), 943–973 (1999)CrossRefGoogle Scholar
  32. 32.
    B. Widrow et al., Adaptive noise cancelling: principles and applications. Proc. IEEE 63(12), 1692–1716 (1975)CrossRefGoogle Scholar
  33. 33.
    Z. Ren, Y. Zou, Z. Zhang, Y. Hu, in Fast Extraction of Somatosensory Evoked Potential Using RLS Adaptive Filter Algorithms. 2nd International Congress on Image and Signal Processing, 2009, CISP’09 (IEEE, 2009), pp. 1–4Google Scholar
  34. 34.
    K. Talele, A. Shrivastav, K. Utekar, A. Deshpande, in LMS filter for Noise Cancellation Using Simulink. Third International Conference on Digital Image Processing (ICDIP 2011), vol. 8009. (International Society for Optics and Photonics, 2011), p. 80093 KGoogle Scholar
  35. 35.
    M.M. Mahajan, S. Godbole, Design of least mean square algorithm for adaptive noise canceller. Int. J. Adv. Eng. Sci. Technol. 5, 172–176 (2011)Google Scholar
  36. 36.
    H.-M. Park, S.-H. Oh, S.-Y. Lee, Adaptive noise cancelling based on independent component analysis. Electron. Lett. 38(15), 832–833 (2002)CrossRefGoogle Scholar
  37. 37.
    A. Sahu, S. K. Hota, in Performance Comparison of 2-DOF PID Controller Based on Moth-flame Optimization Technique for Load Frequency Control of Diverse Energy Source Interconnected Power System. Technologies for Smart-City Energy Security and Power (ICSESP) (IEEE, 2018), pp. 1–6Google Scholar
  38. 38.
    H. Senberber, A. Bagis, in Fractional PID Controller Design for Fractional Order Systems Using ABC Algorithm. Electronics, 2017 (IEEE, 2017), pp. 1–7Google Scholar
  39. 39.
    K. Jagatheesan, B. Anand, K.N. Dey, A.S. Ashour, S.C. Satapathy, Performance evaluation of objective functions in automatic generation control of thermal power system using ant colony optimization technique-designed proportional–integral–derivative controller. Electr. Eng. 100(2), 895–911 (2018)CrossRefGoogle Scholar
  40. 40.
    B. Yaghooti, H. Salarieh, Robust adaptive fractional order proportional integral derivative controller design for uncertain fractional order nonlinear systems using sliding mode control. Proc. Inst. Mech. Eng., Part I: J. Syst. Control Eng. 232(5), 550–557 (2018)Google Scholar
  41. 41.
    W. Liao, Z. Liu, S. Wen, S. Bi, D. Wang, in Fractional PID Based Stability Control for a Single Link Rotary Inverted Pendulum. International Conference on Advanced Mechatronic Systems (ICAMechS), 2015 (IEEE, 2015), pp. 562–566Google Scholar
  42. 42.
    R. Munje, B. Patre, A. Tiwari, Sliding mode control, in Investigation of Spatial Control Strategies with Application to Advanced Heavy Water Reacto. (Springer Singapore, Singapore, 2018), pp. 79–91Google Scholar
  43. 43.
    R.A. DeCarlo, S.H. Zak, G.P. Matthews, Variable structure control of nonlinear multivariable systems: a tutorial. Proc. IEEE 76(3), 212–232 (1988)CrossRefGoogle Scholar
  44. 44.
    J. Guldner, V.I. Utkin, Sliding mode control for gradient tracking and robot navigation using artificial potential fields. IEEE Trans. Robot. Autom. 11(2), 247–254 (1995)CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of EngineeringGerman University of Technology in OmanMuscatOman

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