UDC 004.8:621.43:62-52
DOI 10.36461/NP.2026.77.1.010

ARTIFICIAL INTELLIGENCE FOR MODELING AND CONTROL OF INTERNAL COMBUSTION ENGINES
S.V. Kalachin, Doctor of Technical Sciences, Associate Professor
Penza State Agrarian University, Penza, Russia, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

This article deals with the application of Artificial Intelligence (AI) for the tasks of mathematical modeling and control of internal combustion engines (ICE). The relevance of the current research points out increasing demand for energy efficiency, environmental friendliness, and reliability of modern engines, as well as the need to provide interpretability of control algorithms used in intelligent systems. The theoretical basis of the research includes physical and thermodynamic models of ICE, which are confirmed by experimental data and widely used in engineering practice. The research is aimed at adapting these models to conditions of lack of knowledge based on the Mamdani algorithm. The fuzzy model of the engine formalizes the cause-and-effect correlation between exhaust gas temperature, effective power, indicated efficiency, and the supply parameters of fuel activating components, and leads to uncertainty and nonlinearity of real operating conditions. It was noted that the fuzzy logic provides more stable and intuitively interpretable control compared to predictive control methods. Particular attention is drawn to the issues of description and visualization of control processes. A software prototype has been developed in Python with a graphical user interface, which displays in real time the dynamics of the main engine parameters, the composition of the activator, and color indication of overload conditions. The obtained results demonstrate the possibility of practical application of the proposed fuzzy model in real ICE control systems and confirm the prospects of integrating AI methods in engineering.
Keywords: artificial intelligence, internal combustion engine, fuzzy logic, Mamdani algorithm, mathematical modeling, control system.

Publication information View article