Abstract
We describe the use of Ant Colony Optimization (ACO) for the ball and beam control problem, in particular for the problem of tuning a fuzzy controller of the Sugeno type. In our case study the controller has four inputs, each of them with two membership functions; we consider the intersection point for every pair of membership functions as the main parameter and their individual shape as secondary ones in order to achieve the tuning of the fuzzy controller by using an ACO algorithm. Simulation results show that using ACO and coding the problem with just three parameters instead of six, allows us to find an optimal set of membership function parameters for the fuzzy control system with less computational effort needed.
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Naredo, E., Castillo, O. (2011). ACO-Tuning of a Fuzzy Controller for the Ball and Beam Problem. In: Batyrshin, I., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2011. Lecture Notes in Computer Science(), vol 7095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25330-0_6
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DOI: https://doi.org/10.1007/978-3-642-25330-0_6
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