🤖 AI Summary
This paper addresses the ambiguous definitions of complexity and coupling in industrial Control and Automation Systems (iCAS), where physical-centric interpretations cause conceptual confusion. Methodologically, it pioneers a functional-domain–based theoretical framework, grounding complexity and coupling in functional relationships—not physical structure—and integrates empirical evidence from software engineering, industrial automation, and mechanical design via functional domain analysis and inductive reasoning. Key contributions include: (1) exposing the fundamental limitations of physics-based definitions; (2) demonstrating that coupling amplifies complexity through functional dependencies—not system scale; and (3) proving that functional decoupling substantially reduces complexity. These findings challenge conventional software-engineering notions of coupling and establish a cross-disciplinary, operationally grounded theoretical foundation for iCAS design.
📝 Abstract
This paper provides a precise and scientific definition of complexity and coupling, grounded in the functional domain, particularly within industrial control and automation systems (iCAS). We highlight the widespread ambiguity in defining complexity and coupling, emphasizing that many existing definitions rooted in physical attributes lead to confusion and inconsistencies. Furthermore, we re-exhibit why coupled design inherently increases complexity and how potentially this complexity could be reduced. Drawing on examples from various disciplines, such as software engineering, industrial automation, and mechanical design, we demonstrate that complexity does not necessarily correlate with system size or the number of components, and coupling, unlike common belief in software engineering, actually does not occur in the physical domain but in the functional domain. We conclude that effective design necessitates addressing coupling and complexity within the functional domain.