Skip to main content
SpringerLink
Log in

Adaptive neural model optimized by modified differential evolution for identifying 5-DOF robot manipulator dynamic system

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

Abstract

This paper proposes an adaptive neural network (ANN) model for modeling the nonlinear dynamic of a robot manipulator based on experimental input–output data from the system. The ANN model is created by combining the multilayer perceptron neural network structure and the nonlinear auto-regressive with eXogenous input model and is trained by the modified differential evolution (MDE) algorithm. The effectiveness of the proposed method is evaluated and compared with other algorithms such as the back-propagation algorithm, the traditional differential evolution and the hybrid differential evolution–back-propagation algorithm. The results prove that the proposed ANN model optimized by the MDE algorithm for the 5-DOF robot manipulator dynamic is successfully modeled and performed well.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Anh HPH, Ahn KK (2013) A new approach of the 2-axes pam robot arm identification using neural mimo narx model. Int J Artif Intel Tools 22(01):1–26

    Article  Google Scholar 

  • Basso M, Giarre L, Groppi S, Zappa G (2005) Narx models of an industrial power plant gas turbine. Control Syst Technol IEEE Trans 13(4):599–604

    Article  Google Scholar 

  • Brest J, Bošković B, Greiner S, Žumer V, Maučec MS (2007) Performance comparison of self-adaptive and adaptive differential evolution algorithms. Soft Comput 11(7):617–629

    Article  MATH  Google Scholar 

  • Chauhan N, Ravi V, Chandra DK (2009) Differential evolution trained wavelet neural networks: application to bankruptcy prediction in banks. Expert Syst Appl 36(4):7659–7665

    Article  Google Scholar 

  • Das S, Abraham A, Chakraborty UK, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. Evolut Comput IEEE Trans 13(3):526–553

    Article  Google Scholar 

  • Das H, Jena AK, Nayak J, Naik B, Behera H (2015) A novel pso based back propagation learning-mlp (pso-bp-mlp) for classification. Computational intelligence in data mining, vol 2. Springer, Berlin, pp 461–471

    Google Scholar 

  • Dash CSK, Dehuri S, Cho S-B, Wang G-N (2015) Towards crafting a smooth and accurate functional link artificial neural networks based on differential evolution and feature selection for noisy database. Int J Comput Intel Syst 8(3):539–552

    Article  Google Scholar 

  • Diaconescu E (2008) The use of narx neural networks to predict chaotic time series. Wseas Trans Comput Res 3(3):182–191

    Google Scholar 

  • Gämperle R, Müller SD, Koumoutsakos P (2002) A parameter study for differential evolution. Adv Intel Syst Fuzzy Syst Evol Comput 10:293–298

    Google Scholar 

  • Gaspar CRM, Velázquez-Velázquez JE, Rodríguez JCT, (2015) Identification of a cylindrical robot using recurrent neural networks. Multibody mechatronic systems, pp 381–389. Springer, Berlin

  • Gori M, Tesi A (1992) On the problem of local minima in backpropagation. IEEE Trans Pattern Anal Mach Intel 1:76–86

    Article  Google Scholar 

  • Hovland G, Berglund E, Hanssen S, AB AR (2001) Identification of coupled elastic dynamics using inverse eigenvalue theory. In: International symposium on robotics, vol 19, p 21

  • Huang H-X, Li J-C, Xiao C-L (2015) A proposed iteration optimization approach integrating backpropagation neural network with genetic algorithm. Exp Syst Appl 42(1):146–155

    Article  Google Scholar 

  • Hung Y-C, Lin F-J, Hwang J-C, Chang J-K, Ruan K-C (2015) Wavelet fuzzy neural network with asymmetric membership function controller for electric power steering system via improved differential evolution. Power Electr IEEE Trans 30(4):2350–2362

    Article  Google Scholar 

  • Ilonen J, Kamarainen J-K, Lampinen J (2003) Differential evolution training algorithm for feed-forward neural networks. Neural Process Lett 17(1):93–105

    Article  Google Scholar 

  • Karaboga N, Cetinkaya B, (2005) Performance comparison of genetic and differential evolution algorithms for digital fir filter design. Advances in information systems, pp 482–488. Springer, Berlin

  • Kuschewski JG, Hui S et al (1993) Application of feedforward neural networks to dynamical system identification and control. Control Syst Technol IEEE Trans 1(1):37–49

    Article  Google Scholar 

  • Liu J, Lampinen J (2005) A fuzzy adaptive differential evolution algorithm. Soft Comput 9(6):448–462

    Article  MATH  Google Scholar 

  • Narendra KS, Parthasarathy K (1990) Identification and control of dynamical systems using neural networks. Neural Netw IEEE Trans 1(1):4–27

    Article  Google Scholar 

  • Östring M, Gunnarsson S, Norrlöf M (2003) Closed-loop identification of an industrial robot containing flexibilities. Control Eng Pract 11(3):291–300

    Article  Google Scholar 

  • Pham MT, Gautier M, Poignet P (2001) Identification of joint stiffness with bandpass filtering. In: Robotics and automation, IEEE international conference on, vol 3, IEEE (2001), pp 2867–2872

  • Piotrowski AP (2014) Differential evolution algorithms applied to neural network training suffer from stagnation. Appl Soft Comput 21:382–406

    Article  Google Scholar 

  • Piroddi L, Spinelli W (2003) An identification algorithm for polynomial narx models based on simulation error minimization. Int J Control 76(17):1767–1781

    Article  MathSciNet  MATH  Google Scholar 

  • Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. Evol Comput IEEE Trans 13(2):398–417

    Article  Google Scholar 

  • Ronkkonen J, Kukkonen S, Price KV (2005) Real-parameter optimization with differential evolution. Proc IEEE CEC 1:506–513

  • Sarangi PP, Sahu A, Panda M (2013) A hybrid differential evolution and back-propagation algorithm for feedforward neural network training. Int J Comput Appl 84(14):1–9

    Google Scholar 

  • Son NN, Anh HPH (2015) Adaptive displacement online control of shape memory alloys actuator based on neural networks and hybrid differential evolution algorithm. Neurocomputing 166:464–474

    Article  Google Scholar 

  • Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  • Subudhi B, Jena D (2011) A differential evolution based neural network approach to nonlinear system identification. Appl Soft Comput 11(1):861–871

    Article  Google Scholar 

  • Subudhi B, Jena D (2011) Nonlinear system identification using memetic differential evolution trained neural networks. Neurocomputing 74(10):1696–1709

    Article  Google Scholar 

  • Swevers J, Verdonck W, Schutter JD (2007) Dynamic model identification for industrial robots. Control Syst IEEE 27(5):58–71

    Article  MathSciNet  Google Scholar 

  • Vesterstrøm J, Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Evolutionary computation, congress on, vol 2, IEEE, pp 1980–1987

  • Wang X, Tang Z, Tamura H, Ishii M, Sun W (2004) An improved backpropagation algorithm to avoid the local minima problem. Neurocomputing 56:455–460

    Article  Google Scholar 

  • Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. Evol Comput IEEE Trans 15(1):55–66

    Article  Google Scholar 

Download references

Acknowledgments

This paper was completely supported by the National Foundation for Science and Technology Development (NAFOSTED), under Grant Number 107.01-2015.23, Viet Nam and the DCSELAB, VNU-HCM, Viet Nam.

Author information

Authors and Affiliations

  1. IUHYRA Member, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Nguyen Ngoc Son

  2. FEEE, DCSELAB, HCM City University of Technology - VNU HCM, Ho Chi Minh City, Vietnam

    Ho Pham Huy Anh

  3. FEEE, HCM City University of Technology - VNU HCM, Ho Chi Minh City, Vietnam

    Truong Dinh Chau

Authors
  1. Nguyen Ngoc Son
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ho Pham Huy Anh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Truong Dinh Chau
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ho Pham Huy Anh.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Son, N.N., Anh, H.P.H. & Chau, T.D. Adaptive neural model optimized by modified differential evolution for identifying 5-DOF robot manipulator dynamic system. Soft Comput 22, 979–988 (2018). https://doi.org/10.1007/s00500-016-2401-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2401-x

Keywords

Search

Navigation