EFFICIENCY AND RELIABILITY OF MULTI-OBJECT CONTROL METHODS IN COMPLEX NETWORKS

Abstract

Topicality. Efficient multi-object control in network environments ensures optimal performance and reliability. Due to delays and errors, traditional control methods often face challenges in managing complex, large-scale networks. The aim of the research. This study aims to evaluate and compare the efficiency and reliability of three distinct multi-object control methods: independent control, sequential control with error correction, and simultaneous control with global error correction. Research methods. The research employs mathematical modelling, probabilistic time graphs, and generating functions to develop and analyze the three control methods. Research results. To determine each method's performance, the study considers various factors such as network size, control distance, and error probability. Control distances are categorized into local, adjacent, and distant groups to assess their impact on control efficiency. Independent control, while simple and autonomous, becomes inefficient in larger networks due to insufficient coordination between objects. Sequential control enhances accuracy and reliability through stage-wise verification but faces increased control times in larger networks. Simultaneous control significantly reduces control time by managing all objects concurrently but is sensitive to error frequency, leading to potential delays in high-error environments. The study finds that control distance and network size significantly affect the performance of these methods, with simultaneous control maintaining stable control times in extensive networks, provided error rates are low. Conclusions. Independent control is most suitable for small, localized networks, sequential control is ideal for accuracy-critical applications, and simultaneous control is recommended for large-scale networks requiring rapid control and low error rates. Future research should explore hybrid approaches and the impact of emerging technologies like machine learning and artificial intelligence to further enhance multi-object control efficiency and reliability. This study provides a foundation for optimizing control strategies in increasingly complex network environments.