Skip to main content
Log in

Modeling and stability analysis methods of neutrosophic transfer functions

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Uncertainty is inherent property in actual control systems because parameters in actual control systems are no constants and changeable under some environments. Therefore, actual systems imply their indeterminate parameters, which can affect the control behavior and performance. Then, a neutrosophic number (NN) presented by Smarandache is very easy expressing determinate and/or indeterminate information because a NN p = c + dI is composed of its determinate term c and its indeterminate term dI for c, dR (R is all real numbers), where the symbol “I” denotes indeterminacy. Unfortunately, all uncertain modeling and analysis of practical control systems in existing literature do not provide any concept of NN models and analysis methods till now. Hence, this study firstly proposes a neutrosophic modeling method and defines a neutrosophic transfer function and a neutrosophic characteristic equation. Then, two stability analysis methods of neutrosophic linear systems are established based on the bounded range of all possible characteristic roots and the neutrosophic Routh stability criterion. Finally, the proposed methods are used for two practical examples on the RLC circuit and mass–spring–damper systems with NN parameters. The analysis results demonstrate the effectiveness and feasibility of the proposed methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Chen JQ, Ye J, Du SG (2017a) Scale effect and anisotropy analyzed for neutrosophic numbers of rock joint roughness coefficient based on neutrosophic statistics. Symmetry 9(10):208. https://doi.org/10.3390/sym9100208

    Article  Google Scholar 

  • Chen JQ, Ye J, Du SG, Yong R (2017b) Expressions of rock joint roughness coefficient using neutrosophic interval statistical numbers. Symmetry 9(7):123. https://doi.org/10.3390/sym9070123

    Article  Google Scholar 

  • Czarkowski D, Pujara LR, Kazimierczuk MK (1995) Robust stability of state feedback control of PWM DC-DC push-pull converter. IEEE Trans Ind Electron 42(1):108–111

    Article  Google Scholar 

  • Dazzo JJ, Houpis CH (1995) Linear control system analysis and design, 4th edn. McGraw-Hill, USA

    Google Scholar 

  • Elkaranshawy HA, Bayoumi EHE, Soliman HM (2009) Robust control of a flexible-arm robot using Kharitonov theorem. Electromotion 16:98–108

    Google Scholar 

  • Hote YV, Roy Choudhury D, Gupta JRP (2009) Robust stability analysis PWM push-pull DC-DC converter. IEEE Trans Power Electron 24(10):2353–2356

    Article  Google Scholar 

  • Hote YV, Gupta JRP, Roy Choudhury D (2010) Kharitonov’s theorem and Routh criterion for stability margin of interval systems. Int J Control Autom Syst 8(3):647–654

    Article  Google Scholar 

  • Hussein MT (2005) A novel algorithm to compute all vertex matrices of an interval matrix: computational approach. Int J Comput Inf Sci 2(2):137–142

    Google Scholar 

  • Hussein MT (2010) An efficient computational method for bounds of eigenvalues of interval system using a convex hull algorithm. Arab J Sci Eng 35(1B):249–263

    Google Scholar 

  • Hussein MT (2011) Assessing 3-D uncertain system stability by using MATLAB convex hull functions. Int J Adv Comput Sci Appl 2(6):13–18

    Article  Google Scholar 

  • Hussein MT (2015) Modeling mechanical and electrical uncertain systems using functions of robust control MATLAB Toolbox®3. Int J Adv Comput Sci Appl 6(4):79–84

    Google Scholar 

  • Jiang WZ, Ye J (2016) Optimal design of truss structures using a neutrosophic number optimization model under an indeterminate environment. Neutrosophic Sets Syst 14:93–97

    Google Scholar 

  • Kharitonov VL (1979) Asymptotic stability of an equilibrium position of a family of systems of linear differential equations. Differ Equ 14:1483–1485

    MATH  Google Scholar 

  • Kolev LV (1988) Interval mathematics algorithms for tolerance analysis. IEEE Trans Circuits Syst 35:967–975

    Article  MathSciNet  MATH  Google Scholar 

  • Kong LW, Wu YF, Ye J (2015) Misfire fault diagnosis method of gasoline engines using the cosine similarity measure of neutrosophic numbers. Neutrosophic Sets Systems 8:43–46

    Google Scholar 

  • Meressi T, Chen D, Paden B (1993) Application of Kharitonov’s theorem to mechanical systems. IEEE Trans Autom Control 38(3):488–491

    Article  MathSciNet  MATH  Google Scholar 

  • Precup RE, Preitl S (2006) PI and PID controllers tuning for integral-type servo systems to ensure robust stability and controller robustness. Springer J Electr Eng 88:149–156

    Article  Google Scholar 

  • Smarandache F (1998) Neutrosophy: neutrosophic probability, set, and logic. American Research Press, Rehoboth

    MATH  Google Scholar 

  • Smarandache F (2013) Introduction to neutrosophic measure, neutrosophic integral, and neutrosophic probability. Sitech & Education Publisher, Craiova—Columbus

    MATH  Google Scholar 

  • Smarandache F (2014) Introduction to neutrosophic statistics. Sitech & Education Publishing, Craiova

    MATH  Google Scholar 

  • Ye J (2016a) Multiple-attribute group decision-making method under a neutrosophic number environment. J Intell Syst 25(3):377–386

    Article  Google Scholar 

  • Ye J (2016b) Fault diagnoses of steam turbine using the exponential similarity measure of neutrosophic numbers. J Intell Fuzzy Syst 30:1927–1934

    Article  MATH  Google Scholar 

  • Ye J (2017a) Bidirectional projection method for multiple attribute group decision making with neutrosophic numbers. Neural Comput Appl 28:1021–1029

    Article  Google Scholar 

  • Ye J (2017b) Neutrosophic linear equations and application in traffic flow problems. Algorithms 10(4):133. https://doi.org/10.3390/a10040133

    Article  MathSciNet  Google Scholar 

  • Ye J (2018) Neutrosophic number linear programming method and its application under neutrosophic number environments. Soft Comput 22(14):4639–4646

    Article  Google Scholar 

  • Ye J, Yong R, Liang QF, Huang M, Du SG (2016) Neutrosophic functions of the joint roughness coefficient (JRC) and the shear strength: a case study from the pyroclastic rock mass in Shaoxing City, China. Mathem Problems Eng 2016, Article ID 4825709, 9 pages. http://dx.doi.org/10.1155/2016/4825709

  • Ye J, Chen JQ, Yong R, Du SG (2017) Expression and analysis of joint roughness coefficient using neutrosophic number functions. Information 8(2):69. https://doi.org/10.3390/info8020069

    Article  Google Scholar 

Download references

Acknowledgment

This paper was supported by the National Natural Science Foundation of China (No. 61703280).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jun Ye.

Ethics declarations

Conflict of interest

The authors declare that we have no conflict of interest regarding the publication of this paper.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, J., Cui, W. Modeling and stability analysis methods of neutrosophic transfer functions. Soft Comput 24, 9039–9048 (2020). https://doi.org/10.1007/s00500-019-04434-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04434-0

Keywords

Navigation