Neural Network based Direct MRAC Technique for Improving Tracking Performance for Nonlinear Pendulum System

Authors

  • Alemie Assefa Department of Electrical and Computer Engineering, Debre Berhan University, Debere Berhan, Ethiopia

DOI:

https://doi.org/10.54060/JIEEE/001.02.004

Keywords:

Model reference adaptive control (MRAC), Neural Network (NN), Multilayer Back propagation Neural Network

Abstract

This paper investigates the application of a neural network-based model reference adaptive intelligent controller for controlling the nonlinear systems. The idea is to control the plant by minimizing the tracking error between the desired reference model and the nonlinear system using conventional model reference adaptive con-troller by estimating the adaptation law using a multilayer backpropagation neural network. In the conventional model reference adaptive controller block, the controller is designed to realize the plant output converges to reference model output based on the plant, which is linear. This controller is effective for controlling the linear plant with unknown parameters. However, controlling of a nonlinear system using MRAC in real-time is difficult. The Neural Network is used to compensate the nonlinearity and disturbance of the nonlinear pendulum that is not taken into consideration in the conventional MRAC therefore, the proposed paper can significantly improve the system behaviour and force the system to behave the reference model and reduce the error between the model and the plant output. Adaptive law using Lyapunov stability criteria for updating the controller parameters online has been formulated. The behaviour of the proposed control scheme is verified by developing the simula-tion results for a simple pendulum. It is shown that the proposed neural net-work-based Direct MRAC has small rising time, steady-state error and settling time for a different disturbance than Conventional Direct MRAC adaptive control.

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References

E. Lavretsky and K. A. Wise,” Robust and adaptive control: With aerospace applications, 2013th ed.” London, England: Springer, pp. 317-353, 2012.

N.T. Nguyen, “Model-Reference Adaptive Control” Advanced Textbooks in Control and Signal Processing Springer international publishing, AG , pp.83-123, March 2018.

K. B. Pathak and D. M. Adhyaru, “Survey of model reference adaptive control,” in Nirma University International Conference on Engineering (NUiCONE), Dec 2012.

V.A. Arya, R.B. Aswin, A.E. George “Modified Model Reference Adaptive Control for the Stabilization of Cart Inverted Pendulum System,” International Research Journal of Engineering and Technology (IRJET), IRJET ,vol. 5, no.4, pp. 4592- 4596, April 2018.

R. J. Pawar and B. J. Parvat, “Design and implementation of MRAC and modified MRAC technique for inverted pendulum,” in 2015 International Conference on Pervasive Computing (ICPC), pp. 1-6, Jan 2015.

R. Prakash, R. Anita “Design of Model Reference Adaptive Intelligent Controller Using Neural Network for Nonlinear Systems.”International Review of Automatic Control, vol.4, no. 2, 153-161, March 2011.

M. Caudill,” Neural networks primer, part I,”AI Expert, vol.2, no. 12, pp.46-52, 1987.

P.C. Parks, “Lyapunov Redesign of Model reference adaptive control system”, IEEE Trans on Automatic Control, Vol.AC-11, No.3, pp.362-367, 1966.

C.-C. Hang and P. Parks, “Comparative studies of model reference adaptive control systems,” IEEE Trans. Automat. Contr., vol. 18, no. 5, pp. 419–428, Oct 1973.

P. Swarnkar, S. Jain, and R. K. Nema, “Effect of adaptation gain in model reference adaptive controlled second order system,” Eng. technol. Appl. sci. res., vol. 1, no. 3, pp. 70–75, June 2011.

M. Zhihong, X. H. Yu, K. Eshraghian, et al, “A robust adaptive sliding mode tracking control using an RBF neural network for robotic manipulators,” in Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 5, pp. 2403-2408, 2002.

K. J. Hunt, D. Sbarbaro, R. Żbikowskiet et al, “Neural networks for control systems—A survey,” Automatica (Oxf.), vol. 28, no. 6, pp. 1083–1112, 1992.

M. Zhihong, H. R. Wu, and M. Palaniswami, “An adaptive tracking controller using neural networks for a class of nonlinear systems,” IEEE Trans. Neural Netw., vol. 9, no. 5, pp. 947–955, 1998.

Munadi, M. A. Akbar, T. Naniwa, et al, “Model Reference Adaptive Control for DC motor based on Simulink,” in 2016 6th International Annual Engineering Seminar (InAES), pp.101-106, Aug 2016.

K. Pirabakaran and V. M. Becerra, “Pid autotuning using neural networks and model reference adaptive control,” IFAC proc. vol., vol. 35, no. 1, pp. 451–456, 2002.

K.J. Åström, and B. Wittenmark, “Adaptive control,” Pearson Education India, 2nd Edition, pp.1-38, 2001.

A.V. Duka, S.E. Oltean, & M. Dulau.” Model Reference Adaptive Control and Fuzzy Model Reference Learning Control for the Inverted Pendulum. Comparative Analysis.” International Conference on dynamical systems and control, Venice, Italy, pp.168-173, Nov 2005.

S. Pankaj , J. S.Kumar, R.K Nema, "Comparative analysis of MIT rule and Lyapunov rule in model reference adaptive control scheme." Innovative Systems Design and Engineering , vol.2, no.4, pp. 154-162 , 2011.

Dr. Q. Hamarsheh.”Lectures for Neural networks and Fuzzy logic” at Philadelphia University on 2015/2016.

S. Haykin.”Neural networks a comprehensive Foundation second edition.” Pearson Education McMaster University Hamilton, Ontario, Canada, Pearson Education, pp.1-823, 2001.

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Published

2020-11-25

How to Cite

[1]
A. Assefa, “Neural Network based Direct MRAC Technique for Improving Tracking Performance for Nonlinear Pendulum System”, J. Infor. Electr. Electron. Eng., vol. 1, no. 2, pp. 1–15, Nov. 2020.

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Research Article