Intelligent Control has been motivated originally by the idea of mimicking anthropomorphic functions for the purpose of control and management of large and complex systems. Its modern history can be summarized briefly as learning control and pattern-based control in 1960s, hierarchical and expert control in 1970s, control based on fuzzy logic or neural networks in 1970s and 1980s, and now various algorithms from computational intelligence. Although significant progresses have been achieved in many R&D aspects over the past 50 years, there still exist a huge gap between theories, applications, and objectives for intelligent control. Especially, many problems dealt with by so-called intelligent control methods are neither large nor complex. In this presentation, we will introduce a new mechanism for intelligent control of real-world large and complex problems using the concept of parallel control or parallel management based on the ACP approach, that is, modeling with Artificial systems, analysis by Computational experiments, and control through Parallel execution for complex systems, especially those involving social and human dimensions. Neural networks and other methods in computational intelligence are critical to the successful applications of parallel control and management. Particularly, we will discuss the use of adaptive dynamic programming (ADP), agent-based control (ABC), data-driven decision-making and machine learning in ACP-based controls and some examples of their real-world applications in transportation and production systems.