A Comprehensive Review on Intelligence Control for Complex System

Abstract

Control system intellectualization issues are observed. The need for intellectualization of a diverse variety of systems and control approaches is supported. The hierarchy of intellectual control levels is examined, and various artificial intelligence methods are compared. Intelligence control for complex systems involves using advanced algorithms and techniques, such as artificial intelligence and machine learning, to effectively manage and manipulate complex systems. This includes creating models and simulations to understand the system's behavior, sensing and acquiring real-time data, preprocessing and analyzing the data, making decisions based on the analyzed data and system models, adapting control strategies in real-time, facilitating human-machine interaction, monitoring performance, and optimizing control strategies. The goal is to improve efficiency, safety, reliability, and overall performance of complex systems in various domains.