Dynamical System Algorithm Specification Analysis and Stabilization

  • Charles C. Phiri
  • János Botzheim
  • Cristina Valle
  • Zhaojie JuEmail author
  • Honghai Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10462)


This paper investigates approaches to deliberately designing systems whose controllability can be quantified. Preliminary findings of ongoing research are presented on complex dynamical system control algorithms. The specification analysis and quality of the pressure control algorithm applied to a Topical Negative Pressure Wound Therapy device are conducted, with further discussion on self-regulation mechanism and characterization of both the partially observable and partially controllable workspace represented by the negative pressure chamber. Statistical methods are employed to understand the device physics and fuzzy logic and bacterial memetic algorithm are utilised to explore and optimize the existing algorithms and also extract the rule base.


Specification analysis Fuzzy inference Bacterial memetic algorithm 



The authors would like to acknowledge support from CC Initiative Ltd., project of NSFC (51575412), DREAM EU FP7-ICT (611391), State Key Laboratory of Digital Manufacturing Equipment & Technonlogy (DMETKF2017003), and the exchange program from School of System Design, Kubota Laboratories, Japan.


  1. 1.
    Berrevoet, F., Vanlander, A., Sainz-Barriga, M., Rogiers, X., Troisi, R.: Infected large pore meshes may be salvaged by topical negative pressure therapy. Hernia 17(1), 67–73 (2013)CrossRefGoogle Scholar
  2. 2.
    Borwein, J., Bailey, D.: Mathematics by Experiment: Plausible Reasoning in the 21st Century. A K Peters, Wellesley (2003)zbMATHGoogle Scholar
  3. 3.
    Botzheim, J., Cabrita, C., Kóczy, L.T., Ruano, A.E.: Fuzzy rule extraction by bacterial memetic algorithms. Int. J. Intell. Syst. 24(3), 312–339 (2009)CrossRefzbMATHGoogle Scholar
  4. 4.
    Botzheim, J., Toda, Y., Kubota, N.: Bacterial memetic algorithm for offline path planning of mobile robots. Memet. Comput. 4(1), 73–86 (2012)CrossRefGoogle Scholar
  5. 5.
    Mamdani, E.H.: S.A.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1–13 (1975)CrossRefzbMATHGoogle Scholar
  6. 6.
    Gotoda, H., Shinoda, Y., Kobayashi, M., Okuno, Y., Tachibana, S.: Detection and control of combustion instability based on the concept of dynamical system theory. Phys. Rev. E 89(2), 022910 (2014)CrossRefGoogle Scholar
  7. 7.
    Ju, Z., Liu, H., Xiong, Y.: Fuzzy empirical copula for estimating data dependence structure. Int. J. Fuzzy Syst. 16(2), 160–172 (2014)MathSciNetGoogle Scholar
  8. 8.
    Jung, J.W., Leu, V.Q., Do, T.D., Kim, E.K., Choi, H.H.: Adaptive PID speed control design for permanent magnet synchronous motor drives. IEEE Trans. Power Electron. 30(2), 900–908 (2015)CrossRefGoogle Scholar
  9. 9.
    Phiri, C.C., Ju, Z., Liu, H.: Accelerating humanoid robot learning from human action skills using context-aware middleware. In: Kubota, N., Kiguchi, K., Liu, H., Obo, T. (eds.) ICIRA 2016. LNCS, vol. 9834, pp. 563–574. Springer, Cham (2016). doi: 10.1007/978-3-319-43506-0_49 CrossRefGoogle Scholar
  10. 10.
    Weisstein, E.: Sharkovsky’s theorem. Mathworld-a Wolfram web resource.
  11. 11.
    Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Charles C. Phiri
    • 1
    • 2
    • 3
  • János Botzheim
    • 2
    • 3
    • 4
  • Cristina Valle
    • 2
    • 3
  • Zhaojie Ju
    • 1
    • 5
    Email author
  • Honghai Liu
    • 1
  1. 1.School of ComputingUniversity of PortsmouthPortsmouthUK
  2. 2.CC Initiative Ltd.NewburyUK
  3. 3.CC Initiative Ltd.TokyoJapan
  4. 4.Department of AutomationSzéchenyi UniversityGyőrHungary
  5. 5.School of AutomationWuhan University of TechnologyWuhanChina

Personalised recommendations