With the rapid advancement of artificial intelligence (AI) technology, various patterns of human-AI interaction are emerging across specific application scenarios, transitioning from human-led models to human-AI collaboration and then to AI-led systems. This shift has intensified the tension between AI-driven autonomous systems and human control, raising a range of ethical, legal, and social challenges. Human-AI interaction reflects the continuous evolution of human control mechanisms and the decision-making mechanisms within AI-driven autonomous systems. Developing human-controllable AI and achieving meaningful human control (MHC) has become a vital principle to address these challenges, ensuring ethical alignment and effective governance in AI. MHC is also a critical focus in human-centered AI (HCAI) research and application. This chapter first briefly reviews the background behind the proposal of MHC, providing a detailed explanation of its conceptual definition and theoretical framework, and introducing the “tracking and tracing” dual practice path of MHC. It then explores the integration of MHC with effective human control (EHC) from both macro-level perspectives (ethics and law) and micro-level operational practices. This integration ensures the relative advantages of AI systems through approaches such as MHC certification. Using autonomous vehicles as a case study, the chapter examines the practical implementation of MHC in human-controllable AI systems. Looking ahead, it is essential to enhance interdisciplinary research on the controllability of AI-driven autonomous systems, enhance the ethical and legal awareness among stakeholders, moving beyond simplistic technology design perspectives, and focus on the knowledge construction, complexity interpretation, and influencing factors surrounding human control. By fostering this transition in MHC, the development of human-controllable AI cam be further advanced, delivering human-centered AI systems.