Call for Paper - October 2019 Edition
IJCA solicits original research papers for the October 2019 Edition. Last date of manuscript submission is September 20, 2019. Read More

RTRL Algorithm Based Adaptive Controller for Non-linear Multivariable Systems

Print
PDF
International Journal of Computer Applications
© 2010 by IJCA Journal
Number 5 - Article 17
Year of Publication: 2010
Authors:
K.C. Sindhu Thampatty
M. P. Nandakumar
Elizabeth P. Cheriyan
10.5120/115-230

Sindhu K C Thampatty, M P Nandakumar and Elizabeth P Cheriyan. Article: RTRL Algorithm Based Adaptive Controller for Non-linear Multivariable Systems. International Journal of Computer Applications 1(5):94–101, February 2010. Published By Foundation of Computer Science. BibTeX

@article{key:article,
	author = {K.C. Sindhu Thampatty and M. P. Nandakumar and Elizabeth P. Cheriyan},
	title = {Article: RTRL Algorithm Based Adaptive Controller for Non-linear Multivariable Systems},
	journal = {International Journal of Computer Applications},
	year = {2010},
	volume = {1},
	number = {5},
	pages = {94--101},
	month = {February},
	note = {Published By Foundation of Computer Science}
}

Abstract

The paper presents a new design of adaptive and dynamic neural network-based controller architecture with feedback connection for non-linear multivariable systems. The network is trained on-line at each sampling interval using the desired output trajectory and the training method used is the Real Time Recurrent Learning Algorithm (RTRL). The recurrent network is a fully connected one, with feedback from output layer to the input layer through a delay element. Since the synaptic weights to the neurons are adjusted on-line, this controller has potential applications in real time control also. Moreover, it can be used for both continuous and discrete systems. The simulation results obtained by applying the algorithm to a non-linear multivariable system demonstrate the effectiveness of the proposed method.

Reference

    [1] Al-Zohary T.A, Wahdan, M. A. R. Ghonaimy and A. A. Elshamy.,2002 Adaptive Control of Nonlinear Multivariable Systems Using Neural Networks and Approximate Models,, Jounal of applied sciences
    [2] Chang L.H., 2007. Design of nonlinear controller for bi-axial inverted pendulum, IET Control theory Applications, ,Vol 1, No.4, pp.979-986.
    [3] Daniele Semino and Gabriele Pannocchia, 1999. Robust Multivariable Inverse-Based Controllers: Theory and Application, Ind. Eng. Chem. Res., Vol 38, pp.2375-2382.
    [4] Duan G.R, Yu H.H., 2008. Robust pole assignment in high-order descriptor linear systems via proportional plus derivative state feedback, IET Control theory Applications, Vol 2, No.4, pp.277-287.
    [5] Ender L , Maciel Filho R., 2000. Design of multivariable controller based on neural networks, Elsevier, Computers and Chemical Engineering, Vol 24, pp. 937-943.
    [6] Filho. B, Cabral E, Soares A, 1998 . A new approach to artificial neural networks, IEEE Trans on Neural Networks, ,Vol.9, no. 6, pp.1167- 1179.
    [7] Jagannathan Sarangapani, 1989. Neural Network Control of Nonlinear Discrete-Time, Taylor and Francis ,New M. Young, The Technical Writers Handbook, Mill Valley, CA: University Science,
    [8] Jin. L, Nikiforuk. P, Gupta M., 1995. Approximation of discrete time state space trajectories using dynamic recurrent networks, IEEE Trans on Automatic Control, Vol.40, no.7, pp.1266-1270
    [9] Linkens. D, Nyongesa Y, Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications, IEE Proc. Control Theory App, 134(4), pp 367-385
    [10] Narendra K.S and Parthasarathy. K., 1990. Identification and control of dynamical system using neural networks, IEEE Trans on Neural Networks, Vol.1, no. 6, pp.4-27.
    [11] Panos J. Antsaklis,. 1993. Neural Networks in Control Systems, IEEE Trans on Energy Conversion, Vol.8, no. 1, pp71-77.
    [12] Paul J Webrose . An overview of Neural Networks for control, IEEE Trans on Control Systems, no. 1, pp40-42.
    [13] Pearlmutter. B., 1995. Gradient calculations for Dynamic Recurrent Neural networks: A Survey, IEEE Trans on Neural Networks, Vol.6, no.3, pp 1212
    [14] Simon Haykin, 1994. Neural Networks-A comprehensive Foundation , IEEE Press,
    [15] Sindhu Thampatty K .C., M. P. Nandakumar, Elizabeth. P. Cheriyan, 2009. ANN based Adaptive Controller tuned by RTRL Algorithm for non-linear system control, Proc. of Second International workshop on nonlinear Dynamics and Synchronisation, INDS'09, July 20-21, Klagenfurt, Austria.
    [16] Steepen W. Riche Webrose, 1994. Steepest Descent Algorithm for Neural Network controllers and Filters, IEEE Trans on Neural Networks, Vol.5, no. 2.
    [17] Sun X.M, Zhao. J, Wang. W., 2008. State feedback control for discrete delay systems with controller failures based on average dwell-time method, IET Control theory Applications, Vol 2, No.2, pp.126-132.
    [18] Williams R.J and Zipser D., 1989. A learning algorithm for continually Running fully Recurrent Neural Networks, Neural Computation , Vol 1, pp 552-558.
    [19] Zhang. Y, Chen.G. P, Malik. O.P and Hope, G.S., 1992. An Artificial Neural Network Based Adaptive Power System Stabiliser, IEEE Control System Magazine, Vol.12, no. 4, pp8-10.
    [20] Zhang .B., 2008. Parametric eigen structure assignment by state feedback in descriptor systems, IET Control theory Applications, Vol 2, No.4, pp.303-309.