Hybrid System

Hybrid systems, encompassing systems with both continuous and discrete dynamics, are a focus of current research due to their ability to model complex real-world phenomena across diverse fields. Research emphasizes developing efficient algorithms and model architectures, such as neural networks and rapidly-exploring random trees (RRTs), for tasks like motion planning, control synthesis, and system identification within these hybrid frameworks. These advancements are improving the design and analysis of robotic systems, control systems, and other applications requiring robust handling of discontinuous behavior, leading to safer, more efficient, and more reliable systems. Furthermore, hybrid system modeling is enhancing the understanding and verification of complex interactions in multi-agent systems and other scenarios.