AI - Driven - Closed-Loop Control: A Recipe for Factory of the Future

AI-Driven Closed-Loop Control presents a modern approach to building intelligent, self-adapting factories by combining classical control principles with artificial intelligence and digital twins. Traditional automation systems rely on fixed logic and predefined rules. This book shows how AI transforms closed-loop control into a continuously learning system—one that senses real-world conditions, predicts outcomes, makes decisions, and automatically adjusts operations in real time. The book introduces a three-layer digital twin architecture that enables true closed-loop autonomy: Asset & Process Twin for real-time machine control and stability Operational & System Twin for production flow, quality, and energy optimization Intelligence & Decision Twin where AI models analyze data, simulate outcomes, and guide control actions By connecting these digital twins through feedback loops, AI becomes an active participant in control—not just analytics. The result is a factory that can self-optimize, self-correct, and continuously improve across quality, productivity, maintenance, and sustainability. This book explains: How AI enhances classical closed-loop control systems How digital twins act as the bridge between physical and virtual factories How feedback loops enable predictive, adaptive, and autonomous behavior How human operators collaborate with AI in decision-guided control How factories transition from automation to autonomy Written for engineers, system architects, manufacturing leaders, and advanced students, AI-Driven Closed-Loop Control provides a practical and system-level perspective on designing the Factory of the Future—intelligent, resilient, and data-driven.