Hybrid Dynamical System

Hybrid dynamical systems, encompassing systems with both continuous and discrete dynamics, are studied to model and control complex phenomena across various fields. Current research focuses on developing efficient algorithms for motion planning and control, often employing hybrid architectures that combine physics-based models with data-driven approaches like neural networks (e.g., Physics-Informed Neural Networks) or leveraging techniques such as iterative Linear Quadratic Regulators and Rapidly-exploring Random Trees. These advancements improve accuracy, robustness, and scalability in applications ranging from autonomous vehicle control and robotics to ecological modeling and smart contract verification, enabling more precise predictions and safer, more efficient system designs.