Intelligent Control of a Laboratory Industrial System (FESTO MPS PA)

Abstract

The paper investigates the application of genetic algorithms, fuzzy logic, and fuzzy sliding mode to improve control in complex systems, with a specific focus on the FESTO controlled system. Genetic algorithms are utilized to determine the controller parameters, while fuzzy logic and fuzzy sliding mode are employed to handle uncertainty and dynamically adapt control settings. The study delves into the theoretical foundations of these methodologies, outlines the research methodology, and scrutinizes the results. The findings underscore that incorporating these techniques enhances the adaptability and efficiency of the FESTO control system in intricate and uncertain environments. This research lays the groundwork for innovative strategies aimed at enhancing control in complex systems.