Multi-agent systems consist of multiple intelligent agents that interact locally to achieve global tasks. The fundamental elements are dynamics, describing the motion of agents; the network, showing how agents connect and interact; and control, which involves each agent's decision-making to ensure system resilience and achieve a global goal. Control theory in multi-agent systems focuses on designing algorithms and frameworks to coordinate these agents' behaviors. Key aspects include consensus control (agents reaching agreement), coverage control (agents covering an area), and formation control (agents maintaining a specific configuration). These control strategies leverage the network structure and dynamics to ensure efficient and effective collective behavior, with applications in areas like sensor networks, robotics, and social systems.