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Control Strategy and Simulation for a Class of Nonlinear Discrete Systems with Neural Network

  • Peng LiuEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 924)

Abstract

A PID algorithm based on multi-layer neural network training is presented in this paper. The indirect automatic tuning controller for nonlinear discrete systems adopts a learning algorithm. The problem is to select bounded control so that the system output is as close as possible to the required value. Finally, an example is given to show that the proposed controller is effective.

Keywords

Neural network Automatic tuning Nonlinear discrete systems PID control 

References

  1. 1.
    Al-Assadi, S.A.K., Al-Chalabi, L.A.M.: Optimal gain for proportional integral derivation feedback. IEEE Control Syst. Mag. 7(2), 16–19 (2007)Google Scholar
  2. 2.
    Hwang, C., Hsiao, C.-Y.: Solution of a non-convex optimization arising in PI/PID control design. Automatica 38(6), 1895–1904 (2012)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Daley, S., Li, G.P.: Optimal PID tuning using direct search algorithms. Comput. Control Eng. J. 10(3), 51–56 (2009)Google Scholar
  4. 4.
    Juditsky, A., Hjalmarsson, H., Benveniste, A., Delyon, B., Ljung, L.: Nonlinear black-box models in system identification. Mathematical foundations. Automatica 31(3), 1725–1750 (2015)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Narendra, K.S.: Neural networks for control: theory and practice: mathematical foundations. Proc. IEEE 84(1), 1385–1406 (2016)Google Scholar
  6. 6.
    Huang, S.N., Tan, K.K., Lee, T.H.: A combined PID/adaptive controller for a class of nonlinear systems. Automatica 37(6), 611–618 (2011)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Zhao, Y.: On-line neural network learning algorithm with exponential convergence rate. Electron. Lett. 32(1), 1381–1382 (2016)Google Scholar
  8. 8.
    Zhou, G., Si, J.: Advanced neural network training algorithm with reduced complexity based on Jacobian deficiency. IEEE Trans. Neural Netw. 9(3), 448–453 (2017)CrossRefGoogle Scholar
  9. 9.
    Parisi, R., Di Claudio, E.D., Orlandi, G., Rao, B.D.: A generalized learning paradigm exploiting the structure of feedforward neural networks. IEEE Trans. Neural Netw. 7(2), 1450–1459 (2016)Google Scholar
  10. 10.
    Hagan, M.T., Menhaj, M.B.: Training feedforward neural networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(1), 95–99 (2014)Google Scholar
  11. 11.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations of back-propagation errors. Nature 323(2), 533–536 (2016)zbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Communication EngineeringChongqing College of Electronic EngineeringChongqingChina

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