InECCE2019 pp 167-178 | Cite as

Elimination-Dispersal Sine Cosine Algorithm for a Dynamic Modelling of a Twin Rotor System

  • Shuhairie Mohammad
  • Mohd Falfazli Mat Jusof
  • Nurul Amira Mhd Rizal
  • Ahmad Azwan Abd Razak
  • Ahmad Nor Kasruddin NasirEmail author
  • Raja Mohd Taufika Raja Ismail
  • Mohd Ashraf Ahmad
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 632)


This paper presents an improved version of Sine Cosine Algorithm (SCA). The original SCA is a simple algorithm and it offers a good accuracy. However, for some problems and fitness landscapes, the accuracy achievement of the algorithm is not at optimal. Search agents of the algorithm stuck at the local optima. The proposed new algorithm which is called an Elimination-Dispersal Sine-Cosine Algorithm adopts Elimination-Dispersal (ED) strategy from Bacterial Foraging Algorithm. The ED helps search agents to solve the local optima problem. At the same time, an elitism approach is applied in the proposed algorithm. The elitism ensures some agents continue the next search operation from the currently best found solution. The proposed algorithm is tested on CEC2014 benchmark functions that have various fitness landscapes and properties. The accuracy performance is compared with the original SCA and analyzed. It also is applied to acquire and optimize a dynamic model for a Twin Rotor System (TRS). Result of the modelling shows that the proposed algorithm achieves a better accuracy and thus present less modelling error and better dynamic response for the TRS.


Elimination-dispersal Sine cosine algorithm Twin rotor system Dynamic modelling System identification 



This research is financially supported by the Fundamental Research Grant Scheme (FRGS/1/2016/ICT02/UMP/02/1) with the RDU number RDU160103. It is awarded by the Ministry of Higher Education Malaysia (MOHE) through Research and Innovation Department, Universiti Malaysia Pahang (UMP) Malaysia.


  1. 1.
    Pazera M, Buciakowski M, Witczak M (2018) Robust multiple sensor fault-tolerant control for dynamic non-linear systems. Application to the aerodynamical twin-rotor system. Int J Appl Math Comput Sci 28(2):297–308Google Scholar
  2. 2.
    Deniz M, Tatlicioglu E, Bayrak A (2018) Experimental verification of lead-lag compensators on a twin rotor system. Electrical, control and communication engineering. J Riga Tech Univ 14(2):164–171Google Scholar
  3. 3.
    Quanser Inc. (2010) Quanser 2-DOF Helicopter Manual, Technical report, QuanserGoogle Scholar
  4. 4.
    Twin Rotor MIMO 33-007-PCI—Feedback Instruments Ltd.
  5. 5.
    Nasir ANK, Tokhi MO (2015) An improved spiral dynamic optimization algorithm with engineering application. IEEE Trans Syst Man Cybern Syst 45(6):943–954CrossRefGoogle Scholar
  6. 6.
    Nasir ANK, Tokhi MO, Omar ME, Ghani NMA (2014) An improved spiral dynamic algorithm and its application to fuzzy modelling of a twin rotor system. In: 2014 world symposium on computer applications & research (WSCAR), Sousse, 2014, pp 1–6Google Scholar
  7. 7.
    Nasir ANK, Tokhi MO, Ghani NMA (2015) Novel adaptive bacterial foraging algorithms for global optimisation with application to modelling of a TRS. Exp Syst Appl 42(3):1513–1530CrossRefGoogle Scholar
  8. 8.
    Tuan AZ, Rahman NA, Abdul Jalil A, Kamil R (2018) Non-parametric dynamic modeling of twin rotor system using chaos-enhanced stochastic fractal search algorithm. In: Proceedings of the 62nd annual conference of the institute of systems, control and information engineers (ISCIE), 16–18 May 2018. Kyoto, pp 1–4Google Scholar
  9. 9.
    Fotuhi MJ, Hazem ZB, Bingtil Z (2018) Comparison of joint friction estimation models for laboratory 2 DOF double dual twin rotor aero-dynamical system. In: IECON 2018—44th annual conference of the IEEE industrial electronics society. Washington, DC, pp 2231–2236Google Scholar
  10. 10.
    Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Syst 1–14Google Scholar
  11. 11.
    Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67MathSciNetCrossRefGoogle Scholar
  12. 12.
    Liang JJ, Qu BY, Suganthan PN (2014) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Technical report, 201311, Computational Intelligence Laboratory, Zhengzhou University, ZhengzhouGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Shuhairie Mohammad
    • 1
  • Mohd Falfazli Mat Jusof
    • 1
  • Nurul Amira Mhd Rizal
    • 1
  • Ahmad Azwan Abd Razak
    • 1
  • Ahmad Nor Kasruddin Nasir
    • 1
    Email author
  • Raja Mohd Taufika Raja Ismail
    • 1
  • Mohd Ashraf Ahmad
    • 1
  1. 1.Faculty of Electrical & Electronics EngineeringUniversiti Malaysia PahangPekanMalaysia

Personalised recommendations