Abstract
In this study, simplified “beam and ball (BNB) system” is controlled using “intuitionistic fuzzy control (IFC)” method. It is aimed to keep the ball on it in balance by applying DC voltage in different magnitudes to the DC motor of the system called “ball and beam plant”, which has a beam on which a DC motor is attached to the middle point and a ball moving without friction. In order to better observe the effect of this new generation controller applied to the system, parameters such as the torque of the motor, the mass of the beam and the ball, internal and external disturbance, friction etc. were ignored and the system is simplified. The position and velocity of the ball on the beam is taken as input for the controller, while the controller output is chosen as a DC voltage. After I-Fuzzification, I-Inference and I-Defuzzification process are performed in controller block, performance and efficiency of the system are discussed in terms of steady state error, setting time, maximum overshoot, chattering.
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References
Aoustin, Y., Formal'skii, A.M.: Beam-and-ball system under limited control: Stabilization with large basin of attraction. In: 2009 American Control Conference. IEEE (2009)
Lilly, J.H.: Fuzzy Control and Identification. Wiley, Hoboken (2011)
Yu, W., Floriberto, O.: Stability analysis of PD regulation for ball and beam system. In: Proceedings of 2005 IEEE Conference on Control Applications. CCA 2005. IEEE (2005)
Keshmiri, M., et al.: Modeling and control of ball and beam system using model based and non-model based control approaches. Int. J. Smart Sens. Intell. Syst. 5(1) (2012)
Almutairi, N.B., Mohamed, Z.: On the sliding mode control of a ball on a beam system. Nonlinear Dyn. 59(1–2), 221 (2010)
Maalini, P.V.M., Prabhakar, G., Selvaperumal, S.: Modelling and control of ball and beam system using PID controller. In: 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT). IEEE (2016)
Rana, M.A., Zubair, U., Zeeshan, S.: Automatic control of ball and beam system using particle swarm optimization. In: 2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI). IEEE (2011)
Amjad, M., et al.: Fuzzy logic control of ball and beam system. In: 2010 2nd International Conference on Education Technology and Computer, vol. 3. IEEE (2010)
Chang, Y.-H., Wei-Shou, C., Chia-Wen, C.: TS fuzzy model-based adaptive dynamic surface control for ball and beam system. IEEE Trans. Ind. Electron. 60(6), 2251–2263 (2012)
Oh, S.-K., Jang, H.-J., Pedrycz, W.: The design of a fuzzy cascade controller for ball and beam system: a study in optimization with the use of parallel genetic algorithms. Eng. Appl. Artif. Intell. 22(2), 261–271 (2009)
Castillo, O., et al.: New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system. Inf. Sci. 294, 203–215 (2015)
Chang, Y.-H., et al.: Adaptive fuzzy dynamic surface control for ball and beam system. Int. J. Fuzzy Syst. 13(1), 1–7 (2011)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Atanassov, K.T.: Intuitionistic fuzzy set. Fuzzy Sets Syst. 20, 87–96 (1986)
Atanassov, K.T.: On intuitionistic fuzzy sets theory. Springer, Berlin (2014). https://doi.org/10.1007/978-3-642-29127-2
Close, C.M., Frederick, D.K.: Modeling and Analysis of Dynamic Systems. Houghton Mifflin, Boston (1978)
Chaira, T.: Fuzzy Set and Its Extension. Wiley, Hoboken (2019)
Lin, L., Xia, Z.-Q.: Intuitionistic fuzzy implication operators: expressions and properties. J. Appl. Math. Comput. 22(3), 325–338 (2006)
Kutlu, F., Atan, Ö., Silahtar, O.: Intuitionistic fuzzy adaptive sliding mode control of nonlinear systems. Soft. Comput. 24(1), 53–64 (2019). https://doi.org/10.1007/s00500-019-04286-8
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Silahtar, O., Atan, Ö., Kutlu, F., Castillo, O. (2021). Beam and Ball Plant System Controlling Using Intuitionistic Fuzzy Control. In: Shi, Z., Chakraborty, M., Kar, S. (eds) Intelligence Science III. ICIS 2021. IFIP Advances in Information and Communication Technology, vol 623. Springer, Cham. https://doi.org/10.1007/978-3-030-74826-5_22
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DOI: https://doi.org/10.1007/978-3-030-74826-5_22
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