Hysteresis Compensation

Hysteresis compensation aims to mitigate the inaccuracies and unpredictable behavior caused by hysteresis—a phenomenon where a system's output depends not only on the current input but also on its history. Current research focuses on developing accurate hysteresis models using various machine learning approaches, including neural networks (feedforward, recurrent, and operator networks), temporal convolutional networks, and fuzzy logic systems, often applied to specific systems like soft robots, magnetic devices, and continuum manipulators. These advancements are crucial for improving the precision and reliability of control systems in diverse fields, ranging from robotics and medical devices to materials science and traffic management. The ultimate goal is to enable more accurate and robust control of systems exhibiting hysteresis, leading to improved performance and efficiency in various applications.