Target Input Impedance

Target input impedance research focuses on controlling and predicting the interaction between a system (e.g., robot, biological tissue, RF network) and its environment by manipulating impedance parameters like stiffness and damping. Current research employs diverse approaches, including impedance control-inspired influence mechanisms, machine learning models (neural networks, Bayesian optimization), and frequency-domain analysis for impedance matching, to achieve desired system behavior and performance. These advancements have significant implications for robotics (improving manipulation and locomotion), medical diagnostics (enhancing disease detection), and engineering (optimizing RF/mm-wave networks), enabling safer, more efficient, and adaptable systems.