A comprehensive review on computational control

Computational control, an emerging interdisciplinary field, integrates numerical modeling, implicit control strategies, and advanced computational methods to enhance the management of complex dynamic systems. This review explores the foundational principles and recent advancements of computational control in solid mechanics, emphasizing its applications across diverse domains, such as flexible-body control, robotics, structural engineering, and artificial intelligence (AI). Key methodologies, including numerical modeling, implicit control design, and computational method solvers, are examined for their role in improving control accuracy, robustness, and real-time decision-making. The integration of AI into computational control frameworks has further expanded its applicability, enabling adaptive and autonomous systems to operate in uncertain environments. Challenges such as computational complexity, scalability, and uncertainty quantification are reviewed, along with emerging solutions leveraging high-performance computing and hybrid AI-driven controllers. This paper highlights the disruptive potential of computational control in advancing modern automation and intelligent systems while outlining future research directions to enhance its theoretical and practical impact.