A Systems-Theoretic Approach to Adaptive Design and Cyber-Physical Intelligence in Complex Engineering Systems - Johnathan R. Whitmore (Academic Reseach Library for International Journal of Computer Science & Information System, 2025)
The increasing complexity of engineered systems, coupled with the integration of adaptive computational intelligence, necessitates a comprehensive framework grounded in systems theory, cybernetics, and feedback-based design methodologies. This research investigates the convergence of systems-theoretic principles, cyber-physical systems (CPS), and adaptive software architectures to elucidate mechanisms for effective problem-solving, decision-making, and resilience in complex engineering contexts. Drawing upon seminal works in systems theory, organizational learning, and design science (Ashby, 1958; Beer, 1995; Argyris, 1976), the study examines the interplay between structural and functional complexity in product development, the dynamics of feedback loops in self-regulating systems, and the evolving role of artificial intelligence in networked and distributed systems. A qualitative synthesis of existing literature, combined with conceptual modeling of cyber-physical feedback architectures, provides a detailed exploration of mechanisms through which adaptive intelligence can be operationalized in engineering design. The findings highlight that multi-layered feedback mechanisms, informed by both double-loop learning and requisite variety, are central to managing emergent behaviors, enhancing robustness, and supporting design creativity. Furthermore, the integration of machine learning and transformer-based anomaly detection models within engineering workflows offers a scalable approach for system monitoring, fault detection, and real-time optimization. The research underscores the critical importance of aligning system objectives, design processes, and adaptive control strategies to achieve resilient, sustainable, and self-aware engineering outcomes. Implications extend across software engineering, cyber-physical integration, and organizational design, suggesting avenues for future research in adaptive, self-learning system architectures and automated feedback mechanisms.