Dynamic Modeling of Nonlinear Systems in Cyber-Physical Environments

Abstract

Complex behavior understanding and prediction relies on dynamic modeling of nonlinear systems in cyber-physical contexts. Critical for improving control systems, anticipating maintenance needs, and bolstering resilience, it permits accurate portrayal of complex interactions between cyber and physical components. Guaranteeing efficient and adaptable performance in networked ecosystems is made possible by this insight. Dynamic modeling of nonlinear systems (DM-NS) in cyber-physical settings has a number of challenges, such as the need to accurately depict complex interdependencies, accommodate changes in real-time, and reduce uncertainty. Advanced modeling techniques are required to capture the subtle behaviours and maintain the accuracy of the models while trying to balance the complicated interactions between physical and cyber components. A novel strategy is suggested to tackle these obstacles: Analysing Nonlinear Systems Cyber-Physical Modeling (ANSC-PM). By integrating complex mathematical models with adaptive learning algorithms, ANSC-PM is able to characterize nonlinear system behaviours, taking into account the dynamic interplay between cyber and physical components. To remain relevant as system dynamics change, the suggested method adjusts in real-time. Numerous disciplines find ANSC-PM useful, such as control system optimization, robotic predictive maintenance, and networked environment resilience enhancement. The effectiveness of ANSC-PM is determined by careful simulation analysis, which provides insight into its possible benefits in improving system performance, flexibility, and resilience in complex cyber-physical settings. Developing a thorough and flexible strategy that is adapted to the intricacies of cyber-physical systems, this research makes a substantial contribution to the progress of dynamic modeling approaches.