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Modeling and Control of Mechatronic Systems Using Fuzzy Logic


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DOI: https://doi.org/10.15866/ireaco.v7i1.1291

Abstract


The presented paper deals with an effective approach of the robust controller design based on the fuzzy logic. The proposed controller design is verified on the case study of the selected mechatronic system. All presented results are reached in co-simulation of two different modeling environments - Matlab-Simulink and MSC Adams. MSC Adams is used for the dynamics of the mechatronic system, Matlab-Simulink for the control part of the co-simulation expressed by the fuzzy controller, respectively.
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Keywords


Control System; Co-Simulation; Fuzzy Controller; Fuzzy Logic; Matlab-Simulink; Mechatronic System; MSC Adams; Robotic Arm; Soft Computing

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References


Ackermann, J. 1993. Robust Control: Systems with Uncertain Physical Parameters, Springer-Verlag, London, UK.

Ciganek, J. and Kozak, S. 2008. Robust controller design using algebraic theory. Int. conf. Cybernetics and informatics, Zdiar, Slovak Republic.

Danko, J., Bugar, M. and Stanak, V. 2011. Energy analysis of hybrid power source during vehicle motion. - DOI:10.2478/v10228-011-0006-z. In: Scientific Proceedings Faculty of Mechanical Engineering STU Bratislava. - ISSN 1338-1954. - Vol. 19/2011. - Bratislava : Nakladatelstvo STU, pp. 37-42.
http://dx.doi.org/10.2478/v10228-011-0006-z

Oppenheim, A. M. and Schaffer, R.W. 1989. Discrete-Time Signal Processing, Prentice-Hall, Englewood Cliffs.

Schweizer, B. and Sklar, A. 1983. Probabilistic Metric Spaces, North-Holland, New York.

Vysoky, P. 1996. Fuzzy rizeni , ISBN-80-01-01429-8., CVUT, Praha.

Ciganek, J. and Noge, F. 2013. Fuzzy Logic Control of Mechatronic Systems. IEEE Int. conf. on Process Control, Strbske Pleso, Slovak Republic.
http://dx.doi.org/10.1109/pc.2013.6581428

Menghal, P.M., Jaya Laxmi, A., Adaptive neuro-fuzzy inference system (ANFIS) based simulation of induction motor drives, (2012) International Review on Modelling and Simulations (IREMOS), 5 (5), pp. 2007-2016.

Sabri, N., Aljunid, S.A., Salim, M.S., Badlishah, R.B., Kamaruddin, R., Abd Malek, M.F., Fuzzy inference system: Short review and design, (2013) International Review of Automatic Control (IREACO), 6 (4), pp. 441-449.

Jebelli, A., Yagoub, M.C.E., Dhillon, B.S., Fuzzy-based system for efficient and cost-effective control of light power consumption in autonomous vehicles, (2013) International Review of Automatic Control (IREACO), 6 (4), pp. 489-493.


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