A Multilayered Cognitive Control Architecture for Dynamic Object Management Using Recursive Feedback and Predictive Adaptation

In this paper, we introduce the Recursive Feedback and Predictive Adaptation (RFPA) architecture, a novel cognitive control system designed to enhance adaptability and foresight in dynamic, high-stakes environments. The significance of this study lies in its ability to bridge the gap between traditional control systems and next-generation cognitive frameworks, ensuring real-time decision-making with improved predictive accuracy. The primary aim is to develop a multilayered architecture that integrates recursive feedback, Bayesian reasoning, and deep predictive modeling for superior adaptability. The research methodology involves a layered approach comprising a Perceptual Feedback Layer for data processing, a Predictive Modeling Layer for forecasting, a Reasoning and Knowledge Integration Layer for decisionmaking, and an Adaptive Execution Layer for real-time control. Experimental results demonstrate that RFPA enhances system resilience and responsiveness, significantly reducing variability in industrial process simulations. The findings underscore the potential of RFPA to revolutionize cognitive control by enabling proactive rather than reactive system responses.