Big Data-Driven Intelligent Control System Design and Optimisation

Abstract

In the burgeoning era of data-driven innovation, the integration of big data technologies with intelligent control systems has emerged as a transformative force, reshaping the landscape of industrial and urban management. This study delves into the intricate design and optimization of such systems, leveraging the vast reservoirs of data to enhance decision-making processes and operational efficiencies. The research begins with a meticulous exploration of data collection and preprocessing techniques, emphasizing the critical role of data quality in the efficacy of control algorithms. Subsequently, the paper elucidates the architecture of intelligent control systems, highlighting the seamless fusion of big data analytics with traditional control methodologies. The optimization phase of the study introduces advanced parameter tuning techniques, tailored to the dynamic and voluminous nature of big data, and evaluates these methods through rigorous performance metrics. Case studies from both industrial and smart city contexts provide tangible evidence of the transformative potential of these systems, showcasing remarkable improvements in productivity and resource management. The findings underscore the pivotal role of big data in elevating the sophistication and adaptability of control systems, paving the way for future innovations in this domain. As we stand on the precipice of a new technological dawn, the promise of big data-driven intelligent control systems beckons with the potential to revolutionize not just industries and cities, but entire societies. The journey from data to decision, as chronicled in this study, is a testament to the boundless possibilities that lie at the intersection of technology and human ingenuity.