The proposed research project aims to improve energy efficiency, safety, and performance of chemical process systems by developing advanced control strategies for networks of chemical reactors operating under switching rules. To achieve this, we propose to design and analyze Model Predictive Control (MPC) methods that ensure stable and optimal operation when the system transitions between different modes of operation. The project addresses a general and practically relevant problem that arises in many industrial situations such as chemical and biochemical plants, polymerization units, and energy-related processes, where reactors frequently switch between operational phases (heating, reaction, cooling, etc.) due to safety, efficiency, or production constraints. Improper switching can lead to inefficiency, instability, or even unsafe conditions. The research will focus on switched systems that may include both stable and unstable modes, with switching governed by the Average Dwell-Time (ADT) condition. Key contributions will include the development of new MPC algorithms and Lyapunov-based stability tools suitable for switching-mode chemical processes. The expected outcome is a set of theoretically sound and practically applicable control techniques that improve the reliability and adaptability of process operations. The project will also enhance the training of young researchers in advanced control theory and contribute to the growing research capacity at STU. Tím projektu vytvorí systém, v ktorom budú vizuálne dáta z kamier spracovávané pomocou metód strojového videnia a následne využité na identifikáciu dynamiky systému. Takto získané modely budú slúžiť ako základ pre návrh optimálneho riadenia, ktoré zabezpečí presnejšiu a rýchlejšiu odozvu systému bez potreby ľudského zásahu. Výsledkom projektu bude autonómny systém schopný interpretovať vizuálne dáta a efektívne ich zapojiť do riadiacej slučky. Tento systém prispeje k zvýšeniu efektivity, bezpečnosti a udržateľnosti priemyselných procesov.
Projekt tak prepája oblasti umelej inteligencie, strojového videnia a optimálneho riadenia s potenciálom významne prispieť k rozvoju Priemyslu 4.0 a inteligentnej automatizácie.