Acta Polytechnica Hungarica Vol. 9, No. 6, 2012 – 29 – A Stochastic Approach to Fuzzy Control Károly Nagy*, Szabolcs Divéki*, Péter Odry*, Matija Sokola**, Vladimir Vujičić*** * Subotica Tech - College of Applied Sciences, Marka Oreškovića 16 24000 Subotica, Serbia, e-mail: [nagyk, diveki, odry]@vts.su.ac.rs ** The School of Higher Technical Professional Education in Novi Sad Školska 1, 21000 Novi Sad, Serbia, e-mail: sokola@vtsns.edu.rs *** Department/Institute for Power, Electronics and Communications Engineering, Faculty of Technical Sciences, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia, e-mail: vujicicv@uns.ac.rs Abstract: The paper presents the utilization of low-resolution data for control purposes. The control is based on fuzzy logic, with the deployment of stochastic digital low-resolution time arrays. Every control decision contains a degree of imprecision, being derived from measured low-resolution data. The imprecision is eliminated by stochastic noise superimposed during the data gathering, while the negative effects of noise are suppressed both by the fuzzy nature of the decision-making process and by the energy inertia in the controlled object. The proposed stochastic fuzzy control is extremely fast, robust and so simple that it practically does not need a microprocessor. This approach is validated by a simulation of holding upright an inverse pendulum. Keywords: fuzzy control; fuzzy inference systems; approximate reasoning; fuzzification; alpha-cuts; stochastic 1 Introduction Fuzzy logic and fuzzy reasoning have been shown to be a very effective approach in various control applications, especially when the control problem is multi- dimensional; when the plant model is unknown or time-varying; and/or when the feedback measured data are unreliable or unavailable [1]. In many control approaches, the measured feedback is extensively processed in order to eliminate measurement uncertainties and other errors, and such a processed feedback signal is used in the chosen control algorithm. Such processing of high-resolution data either puts further demands on processing capabilities or forces the reduction of the refresh rate of the controller output [2]. Hence, the utilization of accurate high- resolution data may become unsuitable for the control of fast multi-variable processes.