Russian Federation
A network action model of mechatronic devices of an automated module designed for precision surface conditioning of aluminum cards is presented. Given the asynchronous and stochastic nature of parallel technological processes, the authors implement an integrated approach at the interface of fuzzy Petri networks and expert product systems. The paper formalizes in detail the interaction of key active elements: a cleanup unit, a vision system and an industrial robot. Special attention is paid to the development of a decision-making algorithm for stabilizing the temperature mode of a lamp furnace. By applying trapezoidal membership functions and forming a linguistic rule base, a flexible relationship is established between the speed of the conveyor and the angle of rotation of the servomotor valve. The scientific novelty is in the integration of network and production modeling, which makes it possible to take into account the parallelism and uncertainty of system parameters with a lack of accurate mathematical data. The obtained results of modeling confirm the effectiveness of the proposed tools for the design and control of modern intelligent production modules.
model, function, membership, temperature, lamp furnace, servomotor, design
1. Mai TV, Alattas KA, Bouteraa Y, Rahmani R, Fekih A, Mobayen S, Assawinchaichote W. Optimized fuzzy enhanced robust control design for a Stewart parallel robot. Mathematics. 2022;10(11):1917. DOI:https://doi.org/10.3390/math10111917.
2. Phan BK. Application of fuzzy logic in robot control for mechanical processing. ResearchGate; 2025. DOIhttps://doi.org/10.15625/2525-2518/18069
3. Leonenkov AV. Fuzzy modeling in MATLAB and fuzzy TECH. St. Petersburg: BHV-Petersburg; 2005.
4. Borisov VV, Kruglov VV, Fedulov FS. Fuzzy models and networks. Moscow: Telecom; 2012.
5. Li Z, Zhou M, Wu N. A Survey and Comparison of Petri Net-Based Deadlock Prevention Policies for Flexible Manufacturing Systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2008;38(2):173–188. DOI:https://doi.org/10.1109/TSMCC.2007.913920.
6. Ahmedov MA, Mustafayev VA, Atayev GN, Rahimov ShR. Simulation of dynamic enterprise processes with application of the modified fuzzy Petri net. Advances in Intelligent Systems and Computing. 2017;502:913-921.
7. Mustafayev VA, Zeynalabdiyeva IS, Kravets OJ. Control model of parallel functioning production modules as fuzzy Petri nets. Journal of Physics: Conference Series. 2021:1074-1079. DOIhttps://doi.org/10.1088/1742-6596/2094/2/022003.
8. Vasu MB, Pavan Kumar YV, Ratna Kumari UV. Fuzzy logic intelligent controlling concepts in industrial furnace temperature process control. 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT). Ramanathapuram: India; 2012. DOI:https://doi.org/10.1109/ICACCCT.2012.6320801.
9. Mustafayev V, Salmanova M, Budaqov I. Decision-making model for determining the intensity of a processing center under conditions of uncertainty. 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE; 2024. doi:https://doi.org/10.1109/CoDIT62066.2024.10708297.
10. Anjaneyulu SSSR, Rajasekaran KS. Design and implementation of fuzzy control for industrial robots. International Journal of Computer Applications. 2012;47(17):1-5. DOI:https://doi.org/10.5120/7444-0107.
11. Guliyev HB, Farkhadov ZI, Mammadov JF. System of automatic regulation of reactive power by means of fuzzy logic. Reliability: Theory and Applications. 2015;10(2 (37)):50-58.
12. Mammadov J, Huseynov E, Talibov N, Akhmadova T, Ganjaliyeva J. Development of program tool for expert assessment of innovation projects in the scientific technopark. IFAC-PapersOnLine. 2018;51(30):571-574.
