Linear Time Invariant System

Linear Time-Invariant (LTI) systems, fundamental models in control theory, describe systems whose behavior doesn't change over time. Current research focuses on improving control strategies for LTI systems, particularly using neural networks (including recurrent and graph neural networks) to design controllers that guarantee stability and performance, even under uncertainty or adversarial disturbances. These advancements are crucial for optimizing control inputs, enhancing robustness, and achieving efficient system identification in various applications, from robotics and autonomous systems to network control and complex dynamical systems. The development of novel algorithms and theoretical frameworks, such as those based on passivity and PAC-Bayesian bounds, is driving progress in this field.