Soft Computing Technique Based Online Identification and Control of Dynamical Systems

  • Rajesh Kumar
  • Smriti Srivastava
  • J. R. P. Gupta
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 530)

Abstract

This paper proposes a scheme for online identification and indirect adaptive control of dynamical systems based on intelligent radial basis function network (RBFN). The need to use intelligent control techniques arises as the conventional control methods like PID fails to perform when there is a non linearity in the system or system is affected by parameter variations and disturbance signals. In order to show the effectiveness of the proposed scheme, the mathematical models of the dynamical systems considered in this paper were assumed to be unknown. Since most real-world systems are highly complex and their precise mathematical descriptions are not available which further makes their control more difficult. These factors laid the foundation for the development of control schemes based on intelligent tools so that such systems can be controlled. One such scheme, based on RBFN, is presented in this paper. The key part of the scheme is the selection of inputs for the controller and in the proposed scheme; the inputs to the controller were taken to be the past values of plant’s as well as of the controller’s outputs along with the externally applied input. A separate RBFN identification model was also setup to operate in parallel with the controller and plant. Simulation study was performed on two dynamical systems and the results obtained show that the proposed scheme was able to provide the satisfactory online control and identification under the effects of both parameter variations and disturbance signals.

Keywords

Radial Basis Function Networks Brushless DC motor Identificationand Adaptive Control Water Bath System Gradient Descent 
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References

  1. 1.
    Behera, L., Kar, I.: Intelligent Systems and control principles and applications. Oxford University Press, Inc. (2010)Google Scholar
  2. 2.
    Cybenko, G.: Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems 2(4), 303–314 (1989)Google Scholar
  3. 3.
    Funahashi, K.I.: On the approximate realization of continuous mappings by neural networks. Neural networks 2(3), 183–192 (1989)Google Scholar
  4. 4.
    Gopal, M.: Digital Cont & State Var Met. Tata McGraw-Hill Education (2012)Google Scholar
  5. 5.
    He, W., David, A.O., Yin, Z., Sun, C.: Neural network control of a robotic manipulator with input deadzone and output constraint. IEEE Transactions on Systems, Man, and Cybernetics: Systems 46(6), 759–770 (2016)Google Scholar
  6. 6.
    Kang, L.L.K., Zhang, S.: Research and application of compound control based on rbf neural network and pid. In: Intelligent System and Knowledge Engineering, 2008. ISKE 2008. 3rd International Conference on. vol. 1, pp. 848–850. IEEE (2008)Google Scholar
  7. 7.
    Liu, Y.J., Li, J., Tong, S., Chen, C.P.: Neural network control-based adaptive learning design for nonlinear systems with full-state constraints (2016)Google Scholar
  8. 8.
    Lu, J., Hu, H., Bai, Y.: Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and adaboost algorithm. Neurocomputing 152, 305–315 (2015)Google Scholar
  9. 9.
    Micchelli, C.A.: Interpolation of scattered data: distance matrices and conditionally positive definite functions. Constructive approximation 2(1), 11–22 (1986)Google Scholar
  10. 10.
    Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. Neural Networks, IEEE Transactions on 1(1), 4–27 (1990) 9Google Scholar
  11. 11.
    Powell, M.J.: Radial basis functions for multivariable interpolation: a review. In: Algorithms for approximation. pp. 143–167. Clarendon Press (1987)Google Scholar
  12. 12.
    Singh, M., Srivastava, S., Gupta, J., Handmandlu, M.: Identification and control of a nonlinear system using neural networks by extracting the system dynamics. IETE journal of research 53(1), 43–50 (2007)Google Scholar
  13. 13.
    Srivastava, S., Singh, M., Hanmandlu, M.: Control and identification of non-linear systems affected by noise using wavelet network. In: Computational intelligence and applications. pp. 51–56. Dynamic Publishers, Inc. (2002)Google Scholar
  14. 14.
    Srivastava, S., Singh, M., Madasu, V.K., Hanmandlu, M.: Choquet fuzzy integral based modeling of nonlinear system. Applied Soft Computing 8(2), 839–848 (2008)Google Scholar
  15. 15.
    Tan, M., Chong, S., Tang, T., Shukor, A.: Pid control of vertical pneumatic artificial muscle system. Proceedings of Mechanical Engineering Research Day 2016 2016, 206–207 (2016)Google Scholar
  16. 16.
    Tanomaru, J., Omatu, S.: Process control by on-line trained neural controllers. Industrial Electronics, IEEE Transactions on 39(6), 511–521 (1992)Google Scholar
  17. 17.
    Turki, M., Bouzaida, S., Sakly, A., M’Sahli, F.: Adaptive control of nonlinear system using neuro-fuzzy learning by pso algorithm. In: Electrotechnical Conference (MELECON), 2012 16th IEEE Mediterranean. pp. 519–523. IEEE (2012)Google Scholar
  18. 18.
    Wang, T., Gao, H., Qiu, J.: A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Transactions on Neural Networks and Learning Systems 27(2), 416–425 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Rajesh Kumar
    • 1
  • Smriti Srivastava
    • 1
  • J. R. P. Gupta
    • 1
  1. 1.Netaji Subhas Institute of TechnologyDwarkaIndia

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