Design of different reference model based model reference adaptive controller for inversed model non-minimum phase system

Design of different reference model based model reference adaptive controller for inversed model non-minimum phase system

Mita PalGautam Sarkar Ranjit Kumar Barai Tamal Roy 

Electrical Engineering Department, Jadavpur University, Kolkata, India

Dept. of Electrical Engineering, MCKV Institute of Engineering, Liluah, Howrah, India

Corresponding Author Email: 
mitapal91@gmail.com
Page: 
75-79
|
DOI: 
https://doi.org/10.18280/mmep.040202
Received: 
| |
Accepted: 
| | Citation

OPEN ACCESS

Abstract: 

This paper demonstrates the unique property of Model Reference Adaptive Controlled (MRAC) system. Reference Model is used in the MRAC control structure plays a major role for the transient characteristics of the output response of controlled plant. An inversion based Non-minimum phase (NMP) system always gives unbounded response, MRAC scheme not only stabilize this inverse NMP system, it also force the output trajectory to follow the path of the reference model. Lyapunov stability theory based control logic has been applied to obtain the required control parameters with adjustable gains.

Keywords: 

Lyapunov Stability Theory, Model Reference Adaptive Control, Non-Minimum Phase System, Reference Model.

1. Introduction
2. Model Reference Adaptive Control
3. MRAC Employing Lyapunov Stability Theory for Second Order Planet
4. Numeral Example for Simulation
5. Simulation Result
6. Simulation Result Analysis
7. Conclusions
  References

[1] Duan M., Ramani S.K., Okwudire C.E. (2015). Tracking control of non-minimum phase system using filtered basis functions: a nurbs-based approach, Proceedings of the ASME 2015 Dynamic Systems and Control Conference, Ohio, USA, pp. 1-10.

[2] Do T.M., Man Z., Jin J., Zhang C., Zheng J., Wang H. (2015). Sliding mode learning control of nonminimum phase non-linear system, International Journal of Robust and Nonlinear Control, Vol. 26, No. 11, pp. 2281-2298. DOI: 10.1002/rnc.3406

[3] Wei Z.B., Ren J.R., Quan Q. (2016). Further results on additive-state-decomposition-based output feedback tracking control for a class of uncertain non-minimum phase nonlinear systems, 2016 28th Chinese Control and Decision Conference (CCDC), Yinchuan, China, pp. 6793-6798.

[4] Orsag M., Korpela C., Bogdan S., Oh P. (2013). Lyapunov based model reference adaptive control for aerial manipulation, International Conference on Aircraft Systems, Atlanta, GA, USA, pp. 1-8.

[5] Ramesh G., Nayak H. (2014). Liapunov’s stability theory-based model reference adaptive control for dc motor, International Journal of Research in Science and Technology, Vol. 4, No. 3, pp. 9-18.

[6] Trajkov T.N., Koppe H., Gabbert U. (2007). Direct model reference adaptive control (MRAC) design and simulation for the vibration suppression of piezoelectric smart structures, Communications in Nonlinear Science and Numerical Simulation, pp. 1896–1909. DOI: 10.1016/ j.cnsns.2007.03.025 [7] Brufau-Penella J., Tsiakmakis K., Laopoulos T., Puig-Vidal M. (2008). Model reference adaptive control for an ionic polymer metal composite in underwater applications, IOP Publishing Ltd,Smart Materials and Structures, Vol. 17, No. 4, pp. 1-9.

[8] Chen K.Y. (2017). Model reference adaptive minimum-energy control for a mechatronic elevator system, Optimal Control Application and Methods, Vol. 38, No. 1, pp. 3-18. DOI: 0.1002/oca.2239

[9] Chelihi A., Chemachema M. (2014). Model reference adaptive control for twin rotor multiple-input and multiple-output system via minimal controller synthesis, Journal of System and Control ngineering, Vol. 228, No. 6, pp. 406-418.

[10] Oltean S.E., Dulau M., Duka A.V. (2015). Model reference adaptive control for slow processes: a case study on level process control, 9th International Conference Interdisciplinary in Engineering, Romania, pp. 629-636.

[11] Tar J.K., Kova´cs L., Taka´cs A´., Taka´cs B., Zentay P., Haidegger T., Rudas I. (2014). Novel design of a model reference adaptive controller for soft tissue operations, 2014 IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA, USA, pp. 2446-2451. DOI: 10.1109/SMC.2014.6974293

[12] Antony A., Nasar A. (2015). Model reference adaptive control on glucose regulation, International Journal of Science and Research (IJSR), Vol. 4, No. 9, pp. 120-122.

[13] Gabriel G., Keerthana P., Gnanasoundharam J. (2016). Comparison of pi controller, model reference adaptive controller and fuzzy logic controller for coupled tank system, Indian Journal of Science and Technology, Vol. 9, No. 12, pp. 1-5, DOI: 10.17485/ijst/2016/v9i12/89930

[14] Nagrath J., Gopal M. (1971). Advances in control system, Control System Engineering, New Age Publication, New Delhi, pp. 767-771.

[15] Annaswami A.M. (1999). Model reference adaptive control, Wiley Encyclopedia of Electrical and ElectronicsEngineering, pp. 1-6. DOI: 10.1002/047134608X.W1022

[16] Pal M., Sarkar G., Barai R.K., Roy T. (2015). Design of adaptive two-degree-of-freedom controller for inversion based reference input tracking of non-minimum phase system, Proceedings of Michel Faraday IET International Summit-2015 (MFIIS-2015), Kolkata, India, pp. 78-83.

[17] Pal M., Sarkar G., Barai R.K., Roy T. (2016). Reference input tracking of inversion based non-minimum phase system using adaptive two-degree-of-freedom control, 2016 IEEE First International Conference on Control, Measurement and Instrumentation, Kolkata, India, pp. 5080-5087.